mteb/MLQuestions · Datasets at Hugging Face

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C200	Bayesian analysis, a method of statistical inference (named for English mathematician Thomas Bayes) that allows one to combine prior information about a population parameter with evidence from information contained in a sample to guide the statistical inference process.
C201	Then if there are an odd number of numbers in the list the median can be found by counting in from either end of the list to the (n + 1)/2nd number. This will be the median. If there are an even number on the list then average the n/2 and the (N + 2)/2 numbers. In general, the median is at position (n + 1)/2.
C202	These software distributions are open source, licensed under the GNU General Public License (v3 or later for Stanford CoreNLP; v2 or later for the other releases).
C203	How to Estimate an Agile/Scrum Story Backlog with PointsThe goal of agile/scrum estimation. A few terms. Set an estimation range. Set some reference points. Estimate stories with planning poker. Estimate bugs, chores, and spikes. Set aside a couple of days. Use the big numbers: 20, 40, 100.More items•
C204	In practical terms, deep learning is just a subset of machine learning. In fact, deep learning technically is machine learning and functions in a similar way (hence why the terms are sometimes loosely interchanged).
C205	Area in TailsConfidence LevelArea between 0 and z-scorez-score50%0.25000.67480%0.40001.28290%0.45001.64595%0.47501.9602 more rows
C206	The Kolmogorov-Smirnov test (K-S) and Shapiro-Wilk (S-W) test are designed to test normality by comparing your data to a normal distribution with the same mean and standard deviation of your sample. If the test is NOT significant, then the data are normal, so any value above . 05 indicates normality.
C207	Clustering is the task of dividing the population or data points into a number of groups such that data points in the same groups are more similar to other data points in the same group than those in other groups. In simple words, the aim is to segregate groups with similar traits and assign them into clusters.
C208	value of the Shapiro-Wilk Test is greater than 0.05, the data is normal. If it is below 0.05, the data significantly deviate from a normal distribution. If you need to use skewness and kurtosis values to determine normality, rather the Shapiro-Wilk test, you will find these in our enhanced testing for normality guide.
C209	Feature Extraction aims to reduce the number of features in a dataset by creating new features from the existing ones (and then discarding the original features). These new reduced set of features should then be able to summarize most of the information contained in the original set of features.
C210	Receptive fields are defined portion of space or spatial construct containing units that provide input to a set of units within a corresponding layer. The receptive field is defined by the filter size of a layer within a convolution neural network.
C211	An Expert system shell is a software development environment. It contains the basic components of expert systems. A shell is associated with a prescribed method for building applications by configuring and instantiating these components.
C212	Bootstrapping is building a company from the ground up with nothing but personal savings, and with luck, the cash coming in from the first sales. The term is also used as a noun: A bootstrap is a business an entrepreneur with little or no outside cash or other support launches.
C213	The standard deviation (SD) measures the amount of variability, or dispersion, from the individual data values to the mean, while the standard error of the mean (SEM) measures how far the sample mean of the data is likely to be from the true population mean. The SEM is always smaller than the SD.
C214	A sampling error is a statistical error that occurs when an analyst does not select a sample that represents the entire population of data and the results found in the sample do not represent the results that would be obtained from the entire population.
C215	Gradient Backward propagation
C216	Word Embeddings or Word vectorization is a methodology in NLP to map words or phrases from vocabulary to a corresponding vector of real numbers which used to find word predictions, word similarities/semantics. The process of converting words into numbers are called Vectorization.
C217	Feature embedding is an emerging research area which intends to transform features from the original space into a new space to support effective learning. Feature embedding aims to learn a low-dimensional vector representation for each instance to preserve the information in its features.
C218	In probability theory and statistics, the gamma distribution is a two-parameter family of continuous probability distributions. With a shape parameter α = k and an inverse scale parameter β = 1/θ, called a rate parameter. With a shape parameter k and a mean parameter μ = kθ = α/β.
C219	Typically by the time the sample size is 30 the distribution of the sample mean is practically the same as a normal distribution. ¯X, the mean of the measurements in a sample of size n; the distribution of ¯X is its sampling distribution, with mean μ¯X=μ and standard deviation σ¯X=σ√n.
C220	It is a rate per unit of time similar in meaning to reading a car speedometer at a particular instant and seeing 45 mph. The failure rate (or hazard rate) is denoted by h(t) and is calculated from h(t) = \frac{f(t)}{1 - F(t)} = \frac{f(t)}{R(t)} = \mbox{the instantaneous (conditional) failure rate.}
C221	The probability that a random variable X X X takes a value in the (open or closed) interval [ a , b ] [a,b] [a,b] is given by the integral of a function called the probability density function f X ( x ) f_X(x) fX(x): P ( a ≤ X ≤ b ) = ∫ a b f X ( x ) d x .
C222	In computer science, binary search, also known as half-interval search, logarithmic search, or binary chop, is a search algorithm that finds the position of a target value within a sorted array. Binary search compares the target value to the middle element of the array.
C223	Multi-task learning (MTL) is a subfield of machine learning in which multiple learning tasks are solved at the same time, while exploiting commonalities and differences across tasks. In the classification context, MTL aims to improve the performance of multiple classification tasks by learning them jointly.
C224	In statistics, the generalized linear model (GLM) is a flexible generalization of ordinary linear regression that allows for response variables that have error distribution models other than a normal distribution.
C225	Some Disadvantages of KNNAccuracy depends on the quality of the data.With large data, the prediction stage might be slow.Sensitive to the scale of the data and irrelevant features.Require high memory – need to store all of the training data.Given that it stores all of the training, it can be computationally expensive.
C226	Covariance is calculated by analyzing at-return surprises (standard deviations from the expected return) or by multiplying the correlation between the two variables by the standard deviation of each variable.
C227	is that maximin is in decision theory and game theory etc, a rule to identify the worst outcome of each possible option to find one's best (maximum payoff) play while minimax is in decision theory, game theory, etc a decision rule used for minimizing the maximum possible loss, or maximizing the minimum gain.
C228	The learning algorithm of the Hopfield network is unsupervised, meaning that there is no “teacher” telling the network what is the correct output for a certain input.
C229	The various metrics used to evaluate the results of the prediction are :Mean Squared Error(MSE)Root-Mean-Squared-Error(RMSE).Mean-Absolute-Error(MAE).R² or Coefficient of Determination.Adjusted R²
C230	Cons of Reinforcement LearningReinforcement learning as a framework is wrong in many different ways, but it is precisely this quality that makes it useful.Too much reinforcement learning can lead to an overload of states, which can diminish the results.Reinforcement learning is not preferable to use for solving simple problems.More items
C231	Multinomial logistic regression is used when the dependent variable in question is nominal (equivalently categorical, meaning that it falls into any one of a set of categories that cannot be ordered in any meaningful way) and for which there are more than two categories.
C232	A node, also called a neuron or Perceptron, is a computational unit that has one or more weighted input connections, a transfer function that combines the inputs in some way, and an output connection. Nodes are then organized into layers to comprise a network.
C233	Today, machines are intelligent because of a science called the Artificial Intelligence. A simple answer to explain what makes a machine intelligent is Artificial Intelligence. AI allows a machine to interact with the environment in an intelligent manner.
C234	Probability sampling allows researchers to create a sample that is accurately representative of the real-life population of interest.
C235	In technical terms, linear regression is a machine learning algorithm that finds the best linear-fit relationship on any given data, between independent and dependent variables. It is mostly done by the Sum of Squared Residuals Method.
C236	An easy guide to choose the right Machine Learning algorithmSize of the training data. It is usually recommended to gather a good amount of data to get reliable predictions. Accuracy and/or Interpretability of the output. Speed or Training time. Linearity. Number of features.
C237	A residual neural network (ResNet) is an artificial neural network (ANN) of a kind that builds on constructs known from pyramidal cells in the cerebral cortex. Residual neural networks do this by utilizing skip connections, or shortcuts to jump over some layers.
C238	The main advantage of CNN compared to its predecessors is that it automatically detects the important features without any human supervision. For example, given many pictures of cats and dogs, it can learn the key features for each class by itself.
C239	Correlation Defined The closer the correlation coefficient is to +1.0, the closer the relationship is between the two variables. If two variables have a correlation coefficient near zero, it indicates that there is no significant (linear) relationship between the variables.
C240	Variance is the measure of how far the data points are spread out whereas, MSE (Mean Squared Error) is the measure of how actually the predicted values are different from the actual values. Though, both are the measures of second moment but there is a significant difference.
C241	8:3514:50Suggested clip · 95 secondsLecture 6.3 — Logistic Regression \| Decision Boundary — [ Machine YouTubeStart of suggested clipEnd of suggested clip
C242	Monte Carlo tree search algorithm
C243	The agent function is a mathematical function that maps a sequence of perceptions into action. The function is implemented as the agent program. The part of the agent taking an action is called an actuator. environment -> sensors -> agent function -> actuators -> environment.
C244	A correlation coefficient that is greater than zero indicates a positive relationship between two variables. A value that is less than zero signifies a negative relationship between two variables. Finally, a value of zero indicates no relationship between the two variables that are being compared.
C245	The conditional probability can be calculated using the joint probability, although it would be intractable. Bayes Theorem provides a principled way for calculating the conditional probability. The simple form of the calculation for Bayes Theorem is as follows: P(A\|B) = P(B\|A) * P(A) / P(B)
C246	AUC - ROC curve is a performance measurement for classification problem at various thresholds settings. ROC is a probability curve and AUC represents degree or measure of separability. By analogy, Higher the AUC, better the model is at distinguishing between patients with disease and no disease.
C247	"The difference between discrete choice models and conjoint models is that discrete choice models present experimental replications of the market with the focus on making accurate predictions regarding the market, while conjoint models do not, using product profiles to estimate underlying utilities (or partworths)
C248	The arithmetic mean is often known simply as the mean. It is an average, a measure of the centre of a set of data. The arithmetic mean is calculated by adding up all the values and dividing the sum by the total number of values. For example, the mean of 7, 4, 5 and 8 is 7+4+5+84=6.
C249	It is a classification technique based on Bayes' Theorem with an assumption of independence among predictors. In simple terms, a Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature.
C250	Average or arithmetic means give us rough estimate about the common values in that set so that the calculations on all the values will be more or less the same.
C251	An ROC curve (receiver operating characteristic curve) is a graph showing the performance of a classification model at all classification thresholds. This curve plots two parameters: True Positive Rate. False Positive Rate.
C252	In statistics, a negatively skewed (also known as left-skewed) distribution is a type of distribution in which more values are concentrated on the right side (tail) of the distribution graph while the left tail of the distribution graph is longer.
C253	Bag of Words just creates a set of vectors containing the count of word occurrences in the document (reviews), while the TF-IDF model contains information on the more important words and the less important ones as well.
C254	AUC and accuracy are fairly different things. For a given choice of threshold, you can compute accuracy, which is the proportion of true positives and negatives in the whole data set. AUC measures how true positive rate (recall) and false positive rate trade off, so in that sense it is already measuring something else.
C255	The main difference between the t-test and f-test is, that t-test is used to test the hypothesis whether the given mean is significantly different from the sample mean or not. On the other hand, an F-test is used to compare the two standard deviations of two samples and check the variability.
C256	The Mann Whitney U test, sometimes called the Mann Whitney Wilcoxon Test or the Wilcoxon Rank Sum Test, is used to test whether two samples are likely to derive from the same population (i.e., that the two populations have the same shape).
C257	The SVM classifier is a frontier which best segregates the two classes (hyper-plane/ line). You can look at support vector machines and a few examples of its working here.
C258	(8) The moment generating function corresponding to the normal probability density function N(x;µ, σ2) is the function Mx(t) = exp{µt + σ2t2/2}.
C259	5 Answers. The Fourier series is used to represent a periodic function by a discrete sum of complex exponentials, while the Fourier transform is then used to represent a general, nonperiodic function by a continuous superposition or integral of complex exponentials.
C260	The one-way analysis of variance (ANOVA) is used to determine whether there are any statistically significant differences between the means of three or more independent (unrelated) groups.
C261	Why the Lognormal Distribution is used to Model Stock Prices Since the lognormal distribution is bound by zero on the lower side, it is therefore perfect for modeling asset prices which cannot take negative values. The normal distribution cannot be used for the same purpose because it has a negative side.
C262	Principal component analysis (PCA) is a technique for reducing the dimensionality of such datasets, increasing interpretability but at the same time minimizing information loss. It does so by creating new uncorrelated variables that successively maximize variance.
C263	Machine learning usually has to achieve multiple targets, which are often conflicting with each other. Multi-objective model selection to improve the performance of learning models, such as neural networks, support vector machines, decision trees, and fuzzy systems.
C264	Deep Learning does this by utilizing neural networks with many hidden layers, big data, and powerful computational resources. In unsupervised learning, algorithms such as k-Means, hierarchical clustering, and Gaussian mixture models attempt to learn meaningful structures in the data.
C265	“Support Vector Machine” (SVM) is a supervised machine learning algorithm which can be used for both classification or regression challenges. Then, we perform classification by finding the hyper-plane that differentiates the two classes very well (look at the below snapshot).
C266	The normal distribution is the most important probability distribution in statistics because it fits many natural phenomena. For example, heights, blood pressure, measurement error, and IQ scores follow the normal distribution. It is also known as the Gaussian distribution and the bell curve.
C267	t-test is used to test if two sample have the same mean. The assumptions are that they are samples from normal distribution. f-test is used to test if two sample have the same variance.
C268	How to Find a Sample Size Given a Confidence Interval and Width (unknown population standard deviation)za/2: Divide the confidence interval by two, and look that area up in the z-table: .95 / 2 = 0.475. E (margin of error): Divide the given width by 2. 6% / 2. : use the given percentage. 41% = 0.41. : subtract. from 1.
C269	The true error rate is statistically defined as the error rate of the classifier on a large number of new cases that converge in the limit to the actual population distribution. It turns out that there are a number of ways of presenting sample cases to a classifier to get better estimates of the true error rate.
C270	These three elements allow you to take a process perspective on the data. Figure 3: The three minimum requirements for process mining: A Case ID, an Activity name and at least one Timestamp column.
C271	There are two types of chi-square tests. A very small chi square test statistic means that your observed data fits your expected data extremely well. In other words, there is a relationship. A very large chi square test statistic means that the data does not fit very well. In other words, there isn't a relationship.
C272	In a 2-by-2 table with cells a, b, c, and d (see figure), the odds ratio is odds of the event in the exposure group (a/b) divided by the odds of the event in the control or non-exposure group (c/d). Thus the odds ratio is (a/b) / (c/d) which simplifies to ad/bc.
C273	In marketing terms, a multi-armed bandit solution is a 'smarter' or more complex version of A/B testing that uses machine learning algorithms to dynamically allocate traffic to variations that are performing well, while allocating less traffic to variations that are underperforming.
C274	Censoring is a form of missing data problem in which time to event is not observed for reasons such as termination of study before all recruited subjects have shown the event of interest or the subject has left the study prior to experiencing an event. Censoring is common in survival analysis.
C275	Null hypothesis are never accepted. We either reject them or fail to reject them. The distinction between “acceptance” and “failure to reject” is best understood in terms of confidence intervals. Failing to reject a hypothesis means a confidence interval contains a value of “no difference”.
C276	ARIMA is an acronym that stands for AutoRegressive Integrated Moving Average. This is one of the easiest and effective machine learning algorithm to performing time series forecasting. In simple words, it performs regression in previous time step t-1 to predict t.
C277	General reporting recommendations such as that of APA Manual apply. One should report exact p-value and an effect size along with its confidence interval. In the case of likelihood ratio test one should report the test's p-value and how much more likely the data is under model A than under model B.
C278	Root Mean Square Error (RMSE) is the standard deviation of the residuals (prediction errors). Residuals are a measure of how far from the regression line data points are; RMSE is a measure of how spread out these residuals are. In other words, it tells you how concentrated the data is around the line of best fit.
C279	Arrange your set of numbers from smallest to largest. Determine which measure of central tendency you wish to calculate. The three types are mean, median and mode. To calculate the mean, add all your data and divide the result by the number of data.
C280	The bootstrap method is a resampling technique used to estimate statistics on a population by sampling a dataset with replacement. It can be used to estimate summary statistics such as the mean or standard deviation. That when using the bootstrap you must choose the size of the sample and the number of repeats.
C281	Statistical significance is a determination that a relationship between two or more variables is caused by something other than chance. Statistical hypothesis testing is used to determine whether the result of a data set is statistically significant.
C282	In distributed training the workload to train a model is split up and shared among multiple mini processors, called worker nodes. Distributed training can be used for traditional ML models, but is better suited for compute and time intensive tasks, like deep learning for training deep neural networks.
C283	Feature Selection vs Dimensionality Reduction While both methods are used for reducing the number of features in a dataset, there is an important difference. Feature selection is simply selecting and excluding given features without changing them. Dimensionality reduction transforms features into a lower dimension.
C284	Bayes theorem provides a way to calculate the probability of a hypothesis based on its prior probability, the probabilities of observing various data given the hypothesis, and the observed data itself. — Page 156, Machine Learning, 1997.
C285	Multiply the Grand total by the Pretest probability to get the Total with disease. Compute the Total without disease by subtraction. Multiply the Total with disease by the Sensitivity to get the number of True positives. Multiply the Total without disease by the Specificity to get the number of True Negatives.
C286	There are 3 main ways of describing the intensity of an activity – vigorous, moderate, and gentle. Vigorous activities tend to make you “huff and puff”.
C287	How to calculate margin of errorGet the population standard deviation (σ) and sample size (n).Take the square root of your sample size and divide it into your population standard deviation.Multiply the result by the z-score consistent with your desired confidence interval according to the following table:
C288	The adjusted R-squared is a modified version of R-squared that has been adjusted for the number of predictors in the model. The adjusted R-squared increases only if the new term improves the model more than would be expected by chance. It decreases when a predictor improves the model by less than expected by chance.
C289	The bootstrap is a tool, which allows us to obtain better finite sample approximation of estimators. The bootstrap is used all over the place to estimate the variance, correct bias and construct CIs etc. There are many, many different types of bootstraps.
C290	Order of training data during training a neural network matters a great deal. If you are training with a mini batch you may see large fluctuations in accuracy (and cost function) and may end up over fitting correlated portions of your mini batch.
C291	In statistics, the logit (/ˈloʊdʒɪt/ LOH-jit) function or the log-odds is the logarithm of the odds where p is a probability. It is a type of function that creates a map of probability values from to. .
C292	An RNN has a looping mechanism that acts as a highway to allow information to flow from one step to the next. Passing Hidden State to next time step. This information is the hidden state, which is a representation of previous inputs. Let's run through an RNN use case to have a better understanding of how this works.
C293	In terms of machine learning, "concept learning" can be defined as: “The problem of searching through a predefined space of potential hypotheses for the hypothesis that best fits the training examples.” — Tom Michell. Much of human learning involves acquiring general concepts from past experiences.
C294	The Delta Rule employs the error function for what is known as Gradient Descent learning, which involves the 'modification of weights along the most direct path in weight-space to minimize error', so change applied to a given weight is proportional to the negative of the derivative of the error with respect to that
C295	LDA is an example of a topic model and belongs to the machine learning toolbox and in wider sense to the artificial intelligence toolbox.
C296	Entropy is simply a measure of disorder and affects all aspects of our daily lives. In fact, you can think of it as nature's tax. Left unchecked disorder increases over time. Energy disperses, and systems dissolve into chaos.
C297	In mathematics, more specifically in the theory of Monte Carlo methods, variance reduction is a procedure used to increase the precision of the estimates that can be obtained for a given simulation or computational effort. For simulation with black-box models subset simulation and line sampling can also be used.
C298	An autoencoder accepts input, compresses it, and then recreates the original input. A variational autoencoder assumes that the source data has some sort of underlying probability distribution (such as Gaussian) and then attempts to find the parameters of the distribution.
C299	Simply put, a random sample is a subset of individuals randomly selected by researchers to represent an entire group as a whole. The goal is to get a sample of people that is representative of the larger population.

C200

Bayesian analysis, a method of statistical inference (named for English mathematician Thomas Bayes) that allows one to combine prior information about a population parameter with evidence from information contained in a sample to guide the statistical inference process.

C201

Then if there are an odd number of numbers in the list the median can be found by counting in from either end of the list to the (n + 1)/2nd number. This will be the median. If there are an even number on the list then average the n/2 and the (N + 2)/2 numbers. In general, the median is at position (n + 1)/2.

C202

These software distributions are open source, licensed under the GNU General Public License (v3 or later for Stanford CoreNLP; v2 or later for the other releases).

C203

How to Estimate an Agile/Scrum Story Backlog with PointsThe goal of agile/scrum estimation. A few terms. Set an estimation range. Set some reference points. Estimate stories with planning poker. Estimate bugs, chores, and spikes. Set aside a couple of days. Use the big numbers: 20, 40, 100.More items•

C204

In practical terms, deep learning is just a subset of machine learning. In fact, deep learning technically is machine learning and functions in a similar way (hence why the terms are sometimes loosely interchanged).

C205

Area in TailsConfidence LevelArea between 0 and z-scorez-score50%0.25000.67480%0.40001.28290%0.45001.64595%0.47501.9602 more rows

C206

The Kolmogorov-Smirnov test (K-S) and Shapiro-Wilk (S-W) test are designed to test normality by comparing your data to a normal distribution with the same mean and standard deviation of your sample. If the test is NOT significant, then the data are normal, so any value above . 05 indicates normality.

C207

Clustering is the task of dividing the population or data points into a number of groups such that data points in the same groups are more similar to other data points in the same group than those in other groups. In simple words, the aim is to segregate groups with similar traits and assign them into clusters.

C208

value of the Shapiro-Wilk Test is greater than 0.05, the data is normal. If it is below 0.05, the data significantly deviate from a normal distribution. If you need to use skewness and kurtosis values to determine normality, rather the Shapiro-Wilk test, you will find these in our enhanced testing for normality guide.

C209

Feature Extraction aims to reduce the number of features in a dataset by creating new features from the existing ones (and then discarding the original features). These new reduced set of features should then be able to summarize most of the information contained in the original set of features.

C210

Receptive fields are defined portion of space or spatial construct containing units that provide input to a set of units within a corresponding layer. The receptive field is defined by the filter size of a layer within a convolution neural network.

C211

An Expert system shell is a software development environment. It contains the basic components of expert systems. A shell is associated with a prescribed method for building applications by configuring and instantiating these components.

C212

Bootstrapping is building a company from the ground up with nothing but personal savings, and with luck, the cash coming in from the first sales. The term is also used as a noun: A bootstrap is a business an entrepreneur with little or no outside cash or other support launches.

C213

The standard deviation (SD) measures the amount of variability, or dispersion, from the individual data values to the mean, while the standard error of the mean (SEM) measures how far the sample mean of the data is likely to be from the true population mean. The SEM is always smaller than the SD.

C214

A sampling error is a statistical error that occurs when an analyst does not select a sample that represents the entire population of data and the results found in the sample do not represent the results that would be obtained from the entire population.

C215

Gradient Backward propagation

C216

Word Embeddings or Word vectorization is a methodology in NLP to map words or phrases from vocabulary to a corresponding vector of real numbers which used to find word predictions, word similarities/semantics. The process of converting words into numbers are called Vectorization.

C217

Feature embedding is an emerging research area which intends to transform features from the original space into a new space to support effective learning. Feature embedding aims to learn a low-dimensional vector representation for each instance to preserve the information in its features.

C218

In probability theory and statistics, the gamma distribution is a two-parameter family of continuous probability distributions. With a shape parameter α = k and an inverse scale parameter β = 1/θ, called a rate parameter. With a shape parameter k and a mean parameter μ = kθ = α/β.

C219

Typically by the time the sample size is 30 the distribution of the sample mean is practically the same as a normal distribution. ¯X, the mean of the measurements in a sample of size n; the distribution of ¯X is its sampling distribution, with mean μ¯X=μ and standard deviation σ¯X=σ√n.

C220

It is a rate per unit of time similar in meaning to reading a car speedometer at a particular instant and seeing 45 mph. The failure rate (or hazard rate) is denoted by h(t) and is calculated from h(t) = \frac{f(t)}{1 - F(t)} = \frac{f(t)}{R(t)} = \mbox{the instantaneous (conditional) failure rate.}

C221

The probability that a random variable X X X takes a value in the (open or closed) interval [ a , b ] [a,b] [a,b] is given by the integral of a function called the probability density function f X ( x ) f_X(x) fX(x): P ( a ≤ X ≤ b ) = ∫ a b f X ( x ) d x .

C222

In computer science, binary search, also known as half-interval search, logarithmic search, or binary chop, is a search algorithm that finds the position of a target value within a sorted array. Binary search compares the target value to the middle element of the array.

C223

Multi-task learning (MTL) is a subfield of machine learning in which multiple learning tasks are solved at the same time, while exploiting commonalities and differences across tasks. In the classification context, MTL aims to improve the performance of multiple classification tasks by learning them jointly.

C224

In statistics, the generalized linear model (GLM) is a flexible generalization of ordinary linear regression that allows for response variables that have error distribution models other than a normal distribution.

C225

Some Disadvantages of KNNAccuracy depends on the quality of the data.With large data, the prediction stage might be slow.Sensitive to the scale of the data and irrelevant features.Require high memory – need to store all of the training data.Given that it stores all of the training, it can be computationally expensive.

C226

Covariance is calculated by analyzing at-return surprises (standard deviations from the expected return) or by multiplying the correlation between the two variables by the standard deviation of each variable.

C227

is that maximin is in decision theory and game theory etc, a rule to identify the worst outcome of each possible option to find one's best (maximum payoff) play while minimax is in decision theory, game theory, etc a decision rule used for minimizing the maximum possible loss, or maximizing the minimum gain.

C228

The learning algorithm of the Hopfield network is unsupervised, meaning that there is no “teacher” telling the network what is the correct output for a certain input.

C229

The various metrics used to evaluate the results of the prediction are :Mean Squared Error(MSE)Root-Mean-Squared-Error(RMSE).Mean-Absolute-Error(MAE).R² or Coefficient of Determination.Adjusted R²

C230

Cons of Reinforcement LearningReinforcement learning as a framework is wrong in many different ways, but it is precisely this quality that makes it useful.Too much reinforcement learning can lead to an overload of states, which can diminish the results.Reinforcement learning is not preferable to use for solving simple problems.More items

C231

Multinomial logistic regression is used when the dependent variable in question is nominal (equivalently categorical, meaning that it falls into any one of a set of categories that cannot be ordered in any meaningful way) and for which there are more than two categories.

C232

A node, also called a neuron or Perceptron, is a computational unit that has one or more weighted input connections, a transfer function that combines the inputs in some way, and an output connection. Nodes are then organized into layers to comprise a network.

C233

Today, machines are intelligent because of a science called the Artificial Intelligence. A simple answer to explain what makes a machine intelligent is Artificial Intelligence. AI allows a machine to interact with the environment in an intelligent manner.

C234

Probability sampling allows researchers to create a sample that is accurately representative of the real-life population of interest.

C235

In technical terms, linear regression is a machine learning algorithm that finds the best linear-fit relationship on any given data, between independent and dependent variables. It is mostly done by the Sum of Squared Residuals Method.

C236

An easy guide to choose the right Machine Learning algorithmSize of the training data. It is usually recommended to gather a good amount of data to get reliable predictions. Accuracy and/or Interpretability of the output. Speed or Training time. Linearity. Number of features.

C237

A residual neural network (ResNet) is an artificial neural network (ANN) of a kind that builds on constructs known from pyramidal cells in the cerebral cortex. Residual neural networks do this by utilizing skip connections, or shortcuts to jump over some layers.

C238

The main advantage of CNN compared to its predecessors is that it automatically detects the important features without any human supervision. For example, given many pictures of cats and dogs, it can learn the key features for each class by itself.

C239

Correlation Defined The closer the correlation coefficient is to +1.0, the closer the relationship is between the two variables. If two variables have a correlation coefficient near zero, it indicates that there is no significant (linear) relationship between the variables.

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Variance is the measure of how far the data points are spread out whereas, MSE (Mean Squared Error) is the measure of how actually the predicted values are different from the actual values. Though, both are the measures of second moment but there is a significant difference.

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8:3514:50Suggested clip · 95 secondsLecture 6.3 — Logistic Regression | Decision Boundary — [ Machine YouTubeStart of suggested clipEnd of suggested clip

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Monte Carlo tree search algorithm

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The agent function is a mathematical function that maps a sequence of perceptions into action. The function is implemented as the agent program. The part of the agent taking an action is called an actuator. environment -> sensors -> agent function -> actuators -> environment.

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A correlation coefficient that is greater than zero indicates a positive relationship between two variables. A value that is less than zero signifies a negative relationship between two variables. Finally, a value of zero indicates no relationship between the two variables that are being compared.

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The conditional probability can be calculated using the joint probability, although it would be intractable. Bayes Theorem provides a principled way for calculating the conditional probability. The simple form of the calculation for Bayes Theorem is as follows: P(A|B) = P(B|A) * P(A) / P(B)

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AUC - ROC curve is a performance measurement for classification problem at various thresholds settings. ROC is a probability curve and AUC represents degree or measure of separability. By analogy, Higher the AUC, better the model is at distinguishing between patients with disease and no disease.

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"The difference between discrete choice models and conjoint models is that discrete choice models present experimental replications of the market with the focus on making accurate predictions regarding the market, while conjoint models do not, using product profiles to estimate underlying utilities (or partworths)

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The arithmetic mean is often known simply as the mean. It is an average, a measure of the centre of a set of data. The arithmetic mean is calculated by adding up all the values and dividing the sum by the total number of values. For example, the mean of 7, 4, 5 and 8 is 7+4+5+84=6.

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It is a classification technique based on Bayes' Theorem with an assumption of independence among predictors. In simple terms, a Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature.

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Average or arithmetic means give us rough estimate about the common values in that set so that the calculations on all the values will be more or less the same.

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An ROC curve (receiver operating characteristic curve) is a graph showing the performance of a classification model at all classification thresholds. This curve plots two parameters: True Positive Rate. False Positive Rate.

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In statistics, a negatively skewed (also known as left-skewed) distribution is a type of distribution in which more values are concentrated on the right side (tail) of the distribution graph while the left tail of the distribution graph is longer.

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Bag of Words just creates a set of vectors containing the count of word occurrences in the document (reviews), while the TF-IDF model contains information on the more important words and the less important ones as well.

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AUC and accuracy are fairly different things. For a given choice of threshold, you can compute accuracy, which is the proportion of true positives and negatives in the whole data set. AUC measures how true positive rate (recall) and false positive rate trade off, so in that sense it is already measuring something else.

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The main difference between the t-test and f-test is, that t-test is used to test the hypothesis whether the given mean is significantly different from the sample mean or not. On the other hand, an F-test is used to compare the two standard deviations of two samples and check the variability.

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The Mann Whitney U test, sometimes called the Mann Whitney Wilcoxon Test or the Wilcoxon Rank Sum Test, is used to test whether two samples are likely to derive from the same population (i.e., that the two populations have the same shape).

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The SVM classifier is a frontier which best segregates the two classes (hyper-plane/ line). You can look at support vector machines and a few examples of its working here.

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(8) The moment generating function corresponding to the normal probability density function N(x;µ, σ2) is the function Mx(t) = exp{µt + σ2t2/2}.

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5 Answers. The Fourier series is used to represent a periodic function by a discrete sum of complex exponentials, while the Fourier transform is then used to represent a general, nonperiodic function by a continuous superposition or integral of complex exponentials.

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The one-way analysis of variance (ANOVA) is used to determine whether there are any statistically significant differences between the means of three or more independent (unrelated) groups.

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Why the Lognormal Distribution is used to Model Stock Prices Since the lognormal distribution is bound by zero on the lower side, it is therefore perfect for modeling asset prices which cannot take negative values. The normal distribution cannot be used for the same purpose because it has a negative side.

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Principal component analysis (PCA) is a technique for reducing the dimensionality of such datasets, increasing interpretability but at the same time minimizing information loss. It does so by creating new uncorrelated variables that successively maximize variance.

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Machine learning usually has to achieve multiple targets, which are often conflicting with each other. Multi-objective model selection to improve the performance of learning models, such as neural networks, support vector machines, decision trees, and fuzzy systems.

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Deep Learning does this by utilizing neural networks with many hidden layers, big data, and powerful computational resources. In unsupervised learning, algorithms such as k-Means, hierarchical clustering, and Gaussian mixture models attempt to learn meaningful structures in the data.

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“Support Vector Machine” (SVM) is a supervised machine learning algorithm which can be used for both classification or regression challenges. Then, we perform classification by finding the hyper-plane that differentiates the two classes very well (look at the below snapshot).

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The normal distribution is the most important probability distribution in statistics because it fits many natural phenomena. For example, heights, blood pressure, measurement error, and IQ scores follow the normal distribution. It is also known as the Gaussian distribution and the bell curve.

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t-test is used to test if two sample have the same mean. The assumptions are that they are samples from normal distribution. f-test is used to test if two sample have the same variance.

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How to Find a Sample Size Given a Confidence Interval and Width (unknown population standard deviation)za/2: Divide the confidence interval by two, and look that area up in the z-table: .95 / 2 = 0.475. E (margin of error): Divide the given width by 2. 6% / 2. : use the given percentage. 41% = 0.41. : subtract. from 1.

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The true error rate is statistically defined as the error rate of the classifier on a large number of new cases that converge in the limit to the actual population distribution. It turns out that there are a number of ways of presenting sample cases to a classifier to get better estimates of the true error rate.

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These three elements allow you to take a process perspective on the data. Figure 3: The three minimum requirements for process mining: A Case ID, an Activity name and at least one Timestamp column.

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There are two types of chi-square tests. A very small chi square test statistic means that your observed data fits your expected data extremely well. In other words, there is a relationship. A very large chi square test statistic means that the data does not fit very well. In other words, there isn't a relationship.

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In a 2-by-2 table with cells a, b, c, and d (see figure), the odds ratio is odds of the event in the exposure group (a/b) divided by the odds of the event in the control or non-exposure group (c/d). Thus the odds ratio is (a/b) / (c/d) which simplifies to ad/bc.

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In marketing terms, a multi-armed bandit solution is a 'smarter' or more complex version of A/B testing that uses machine learning algorithms to dynamically allocate traffic to variations that are performing well, while allocating less traffic to variations that are underperforming.

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Censoring is a form of missing data problem in which time to event is not observed for reasons such as termination of study before all recruited subjects have shown the event of interest or the subject has left the study prior to experiencing an event. Censoring is common in survival analysis.

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Null hypothesis are never accepted. We either reject them or fail to reject them. The distinction between “acceptance” and “failure to reject” is best understood in terms of confidence intervals. Failing to reject a hypothesis means a confidence interval contains a value of “no difference”.

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ARIMA is an acronym that stands for AutoRegressive Integrated Moving Average. This is one of the easiest and effective machine learning algorithm to performing time series forecasting. In simple words, it performs regression in previous time step t-1 to predict t.

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General reporting recommendations such as that of APA Manual apply. One should report exact p-value and an effect size along with its confidence interval. In the case of likelihood ratio test one should report the test's p-value and how much more likely the data is under model A than under model B.

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Root Mean Square Error (RMSE) is the standard deviation of the residuals (prediction errors). Residuals are a measure of how far from the regression line data points are; RMSE is a measure of how spread out these residuals are. In other words, it tells you how concentrated the data is around the line of best fit.

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Arrange your set of numbers from smallest to largest. Determine which measure of central tendency you wish to calculate. The three types are mean, median and mode. To calculate the mean, add all your data and divide the result by the number of data.

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The bootstrap method is a resampling technique used to estimate statistics on a population by sampling a dataset with replacement. It can be used to estimate summary statistics such as the mean or standard deviation. That when using the bootstrap you must choose the size of the sample and the number of repeats.

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Statistical significance is a determination that a relationship between two or more variables is caused by something other than chance. Statistical hypothesis testing is used to determine whether the result of a data set is statistically significant.

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In distributed training the workload to train a model is split up and shared among multiple mini processors, called worker nodes. Distributed training can be used for traditional ML models, but is better suited for compute and time intensive tasks, like deep learning for training deep neural networks.

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Feature Selection vs Dimensionality Reduction While both methods are used for reducing the number of features in a dataset, there is an important difference. Feature selection is simply selecting and excluding given features without changing them. Dimensionality reduction transforms features into a lower dimension.

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Bayes theorem provides a way to calculate the probability of a hypothesis based on its prior probability, the probabilities of observing various data given the hypothesis, and the observed data itself. — Page 156, Machine Learning, 1997.

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Multiply the Grand total by the Pretest probability to get the Total with disease. Compute the Total without disease by subtraction. Multiply the Total with disease by the Sensitivity to get the number of True positives. Multiply the Total without disease by the Specificity to get the number of True Negatives.

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There are 3 main ways of describing the intensity of an activity – vigorous, moderate, and gentle. Vigorous activities tend to make you “huff and puff”.

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How to calculate margin of errorGet the population standard deviation (σ) and sample size (n).Take the square root of your sample size and divide it into your population standard deviation.Multiply the result by the z-score consistent with your desired confidence interval according to the following table:

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The adjusted R-squared is a modified version of R-squared that has been adjusted for the number of predictors in the model. The adjusted R-squared increases only if the new term improves the model more than would be expected by chance. It decreases when a predictor improves the model by less than expected by chance.

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The bootstrap is a tool, which allows us to obtain better finite sample approximation of estimators. The bootstrap is used all over the place to estimate the variance, correct bias and construct CIs etc. There are many, many different types of bootstraps.

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Order of training data during training a neural network matters a great deal. If you are training with a mini batch you may see large fluctuations in accuracy (and cost function) and may end up over fitting correlated portions of your mini batch.

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In statistics, the logit (/ˈloʊdʒɪt/ LOH-jit) function or the log-odds is the logarithm of the odds where p is a probability. It is a type of function that creates a map of probability values from to. .

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An RNN has a looping mechanism that acts as a highway to allow information to flow from one step to the next. Passing Hidden State to next time step. This information is the hidden state, which is a representation of previous inputs. Let's run through an RNN use case to have a better understanding of how this works.

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In terms of machine learning, "concept learning" can be defined as: “The problem of searching through a predefined space of potential hypotheses for the hypothesis that best fits the training examples.” — Tom Michell. Much of human learning involves acquiring general concepts from past experiences.

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The Delta Rule employs the error function for what is known as Gradient Descent learning, which involves the 'modification of weights along the most direct path in weight-space to minimize error', so change applied to a given weight is proportional to the negative of the derivative of the error with respect to that

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LDA is an example of a topic model and belongs to the machine learning toolbox and in wider sense to the artificial intelligence toolbox.

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Entropy is simply a measure of disorder and affects all aspects of our daily lives. In fact, you can think of it as nature's tax. Left unchecked disorder increases over time. Energy disperses, and systems dissolve into chaos.

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In mathematics, more specifically in the theory of Monte Carlo methods, variance reduction is a procedure used to increase the precision of the estimates that can be obtained for a given simulation or computational effort. For simulation with black-box models subset simulation and line sampling can also be used.

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An autoencoder accepts input, compresses it, and then recreates the original input. A variational autoencoder assumes that the source data has some sort of underlying probability distribution (such as Gaussian) and then attempts to find the parameters of the distribution.

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Simply put, a random sample is a subset of individuals randomly selected by researchers to represent an entire group as a whole. The goal is to get a sample of people that is representative of the larger population.