mteb/MLQuestions · Datasets at Hugging Face

_id stringlengths 2 6	text stringlengths 3 395	title stringclasses 1 value
C100	Most data can be categorized into 4 basic types from a Machine Learning perspective: numerical data, categorical data, time-series data, and text.
C101	It's a method of evaluating how well specific algorithm models the given data. If predictions deviates too much from actual results, loss function would cough up a very large number. Gradually, with the help of some optimization function, loss function learns to reduce the error in prediction.
C102	Momentum [1] or SGD with momentum is method which helps accelerate gradients vectors in the right directions, thus leading to faster converging. It is one of the most popular optimization algorithms and many state-of-the-art models are trained using it.
C103	In machine learning and statistics, the learning rate is a tuning parameter in an optimization algorithm that determines the step size at each iteration while moving toward a minimum of a loss function.
C104	Heterogeneity in statistics means that your populations, samples or results are different. It is the opposite of homogeneity, which means that the population/data/results are the same.
C105	Q-learning is an off policy reinforcement learning algorithm that seeks to find the best action to take given the current state. It's considered off-policy because the q-learning function learns from actions that are outside the current policy, like taking random actions, and therefore a policy isn't needed.
C106	Validation set is different from test set. Validation set actually can be regarded as a part of training set, because it is used to build your model, neural networks or others. It is usually used for parameter selection and to avoild overfitting. Test set is used for performance evaluation.
C107	The F-distribution, also known Fisher-Snedecor distribution is extensively used to test for equality of variances from two normal populations. F-distribution got its name after R.A. Fisher who initially developed this concept in 1920s. It is a probability distribution of an F-statistic.
C108	The disadvantages are numerous. Cross-over studies are often of longer duration than parallel-group studies. There may be difficulty in incorporating multiple dosage arms and in dealing with drop-outs; patients who only complete the first evaluation phase contribute little to the analysis.
C109	Gradient boosting is a machine learning technique for regression and classification problems, which produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees. (
C110	The intuition is simple projection. This picture is from wiki. is the observed response and we predict by the linear combination of the explanatory variables which in inside the vector space .
C111	Much of the modern innovations in image recognition is reliant on Deep Learning technology, an advanced type of Machine Learning, and the modern wonder of Artificial Intelligence. For image recognition, the kind of neural network used is called convolutional neural networks.
C112	From Wikipedia, the free encyclopedia. In statistics and signal processing, a minimum mean square error (MMSE) estimator is an estimation method which minimizes the mean square error (MSE), which is a common measure of estimator quality, of the fitted values of a dependent variable.
C113	Frequency distribution in statistics provides the information of the number of occurrences (frequency) of distinct values distributed within a given period of time or interval, in a list, table, or graphical representation. Grouped and Ungrouped are two types of Frequency Distribution.
C114	In order to fit the best intercept line between the points in the above scatter plots, we use a metric called “Sum of Squared Errors” (SSE) and compare the lines to find out the best fit by reducing errors.
C115	To find the interquartile range (IQR), first find the median (middle value) of the lower and upper half of the data. These values are quartile 1 (Q1) and quartile 3 (Q3). The IQR is the difference between Q3 and Q1.
C116	Data is a set of qualitative or quantitative variables – it can be structured or unstructured, machine readable or not, digital or analogue, personal or not. There are “dimensions” that distinguish data from BIG DATA, summarised as the “3 Vs” of data: Volume, Variety, Velocity. Hence, BIG DATA, is not just “more” data.
C117	A decision tree is one of the supervised machine learning algorithms. This algorithm can be used for regression and classification problems — yet, is mostly used for classification problems. A decision tree follows a set of if-else conditions to visualize the data and classify it according to the conditions.
C118	The logarithm is to exponentiation as division is to multiplication: The logarithm is the inverse of the exponent: it undoes exponentiation. When studying logarithms, always remember the following fundamental equivalence: if and only if . Whenever one of these is true, so is the other.
C119	A convolutional stage in a neural network ensures that each part of the neural network has essentially the same edge detector. So even if your data are biased to have edges only in, say, the lower left side of the image set, your connection weights will not reflect this systematic bias.
C120	Normal distribution, also known as the Gaussian distribution, is a probability distribution that is symmetric about the mean, showing that data near the mean are more frequent in occurrence than data far from the mean. In graph form, normal distribution will appear as a bell curve.
C121	The AUC value lies between 0.5 to 1 where 0.5 denotes a bad classifer and 1 denotes an excellent classifier.
C122	Word2Vec takes texts as training data for a neural network. The resulting embedding captures whether words appear in similar contexts. GloVe focuses on words co-occurrences over the whole corpus. Its embeddings relate to the probabilities that two words appear together.
C123	Simply put, an activation function is a function that is added into an artificial neural network in order to help the network learn complex patterns in the data. When comparing with a neuron-based model that is in our brains, the activation function is at the end deciding what is to be fired to the next neuron.
C124	I often draw a distinction between exploratory and explanatory data analysis. Exploratory analysis is what you do to get familiar with the data. Explanatory analysis is what happens when you have something specific you want to show an audience - probably about those 1 or 2 precious gemstones.
C125	1a(1) : of, relating to, resembling, or having a graph that is a line and especially a straight line : straight. (2) : involving a single dimension.
C126	It is easy to check that the MLE is an unbiased estimator (E[̂θMLE(y)] = θ). To determine the CRLB, we need to calculate the Fisher information of the model.
C127	There are two groups of metrics that may be useful for imbalanced classification because they focus on one class; they are sensitivity-specificity and precision-recall.
C128	AUC stands for "Area under the ROC Curve." That is, AUC measures the entire two-dimensional area underneath the entire ROC curve (think integral calculus) from (0,0) to (1,1). Figure 5. AUC (Area under the ROC Curve).
C129	A statistic T = r(X1,X2,··· ,Xn) is a sufficient statistic if for each t, the conditional distribution of X1,X2, ···,Xn given T = t and θ does not depend on θ.
C130	1:314:30Suggested clip · 120 secondsCumulative Frequency Distribution (Less than and More than YouTubeStart of suggested clipEnd of suggested clip
C131	How to train a Machine Learning model in 5 minutesModel Naming — Give Your Model a Name: Let's start with giving your model a name, describe your model and attach tags to your model. Data Type Selection — Choose data type(Images/Text/CSV): It's time to tell us about the type of data you want to train your model.More items
C132	Manipulate data using Excel or Google Sheets. This may include plotting the data out, creating pivot tables, and so on. Analyze and interpret the data using statistical tools (i.e. finding correlations, trends, outliers, etc.). Present this data in meaningful ways: graphs, visualizations, charts, tables, etc.
C133	The second reason you may see validation loss lower than training loss is due to how the loss value are measured and reported: Training loss is measured during each epoch. While validation loss is measured after each epoch.
C134	Data wrangling is the process of gathering, selecting, and transforming data to answer an analytical question. Also known as data cleaning or “munging”, legend has it that this wrangling costs analytics professionals as much as 80% of their time, leaving only 20% for exploration and modeling.
C135	The chi-square statistic can never be negative. What does it mean to obtain a negative value for the chi-square statistic? the null hypothesis is rejected if the observed U is less than or equal to the critical U.
C136	Fisher's exact test obtains its two-tailed P value by computing the probabilities associated with all possible tables that have the same row and column totals. Then, it identifies the alternative tables with a probability that is less than that of the observed table.
C137	International communication (also referred to as the study of global communication or transnational communication) is the communication practice that occurs across international borders. International communication "encompasses political, economic, social, cultural and military concerns".
C138	Artificial neural networks use backpropagation as a learning algorithm to compute a gradient descent with respect to weights. Because backpropagation requires a known, desired output for each input value in order to calculate the loss function gradient, it is usually classified as a type of supervised machine learning.
C139	In most mechanical systems or models, you can determine the degrees of freedom using the following formula:DOF = 6 x (number of bodies not including ground) – constraints.DOF = (6 x 1) – (2 x 5)DOF = 6 x (number of bodies not including ground) – constraints + redundancies.1 = (6 x 1) – 10 + redundancies.More items
C140	Statistical Package for the Social Sciences
C141	In the “Compute Variable” dialog box that opens, enter a name for the new centered variable in the “Target Variable:” text box at the top right. In the “Numeric Expression:” box, write “math-52.65” as shown in Figure 2. Press OK to create the centered variable.
C142	Summary. The probability distribution of a discrete random variable X is a listing of each possible value x taken by X along with the probability P(x) that X takes that value in one trial of the experiment.
C143	Grid-searching is the process of scanning the data to configure optimal parameters for a given model. Grid-searching can be applied across machine learning to calculate the best parameters to use for any given model.
C144	The definition of conditional probability can be rewritten as: P(E ∩F) = P(E\|F)P(F) which we call the Chain Rule. Intuitively it states that the probability of observing events E and F is the. probability of observing F, multiplied by the probability of observing E, given that you have observed F.
C145	According to the (Research Methods for Business Students) book, to assess the relationship between two ordinal variables is by using Spearman's rank correlation coefficient (Spearman's rho) or Kendall's rank-order correlation coefficient (Kendall's tau).
C146	SVM or Support Vector Machine is a linear model for classification and regression problems. It can solve linear and non-linear problems and work well for many practical problems. The idea of SVM is simple: The algorithm creates a line or a hyperplane which separates the data into classes.
C147	The general regression tree building methodology allows input variables to be a mixture of continuous and categorical variables. A decision tree is generated when each decision node in the tree contains a test on some input variable's value. The terminal nodes of the tree contain the predicted output variable values.
C148	Properties of a normal distributionThe mean, mode and median are all equal.The curve is symmetric at the center (i.e. around the mean, μ).Exactly half of the values are to the left of center and exactly half the values are to the right.The total area under the curve is 1.
C149	Traditional machine learning methods such as Naïve Bayes, Logistic Regression and Support Vector Machines (SVM) are widely used for large-scale sentiment analysis because they scale well.
C150	There are three basic concepts in reinforcement learning: state, action, and reward. The state describes the current situation. For a robot that is learning to walk, the state is the position of its two legs. For a Go program, the state is the positions of all the pieces on the board.
C151	Cohen's kappa measures the agreement between two raters who each classify N items into C mutually exclusive categories.¹ A simple way to think this is that Cohen's Kappa is a quantitative measure of reliability for two raters that are rating the same thing, corrected for how often that the raters may agree by chance.
C152	Similarity is the measure of how much alike two data objects are. Similarity in a data mining context is usually described as a distance with dimensions representing features of the objects. A small distance indicating a high degree of similarity and a large distance indicating a low degree of similarity.
C153	From implementation point of view, Huffman coding is easier than arithmetic coding. Arithmetic algorithm yields much more compression ratio than Huffman algorithm while Huffman coding needs less execution time than the arithmetic coding.
C154	Conditional probability is the probability of one event occurring with some relationship to one or more other events. For example: Event A is that it is raining outside, and it has a 0.3 (30%) chance of raining today. Event B is that you will need to go outside, and that has a probability of 0.5 (50%).
C155	Naïve Bayes has a naive assumption of conditional independence for every feature, which means that the algorithm expects the features to be independent which not always is the case. Logistic regression is a linear classification method that learns the probability of a sample belonging to a certain class.
C156	A false positive, also known as Type I error or alpha error, is an error that occurs when a researcher falsely concludes that an effect exists, or when a null hypothesis is rejected even though the null is true. On the basis of these data, the researcher concludes that there is an effect.
C157	Association Rule Mining, as the name suggests, association rules are simple If/Then statements that help discover relationships between seemingly independent relational databases or other data repositories. Most machine learning algorithms work with numeric datasets and hence tend to be mathematical.
C158	Need for Batch Consumption From Kafka Data ingestion system are built around Kafka. They are followed by lambda architectures with separate pipelines for real-time stream processing and batch processing. Real-time stream processing pipelines are facilitated by Spark Streaming, Flink, Samza, Storm, etc.
C159	A variable is a symbol that represents some quantity. A random variable is a value that follows some probability distribution. In other words, it's a value that is subject to some randomness or chance.
C160	To measure the relationship between numeric variable and categorical variable with > 2 levels you should use eta correlation (square root of the R2 of the multifactorial regression). If the categorical variable has 2 levels, point-biserial correlation is used (equivalent to the Pearson correlation).
C161	With binary data the variance is a function of the mean, and in particular is not constant as the mean changes. This violates one of the standard linear regression assumptions that the variance of the residual errors is constant.
C162	Abstract: In the big data era, the data are generated from different sources or observed from different views. These data are referred to as multi-view data. Multi-view Clustering (MvC) has attracted increasing attention in recent years by aiming to exploit complementary and consensus information across multiple views.
C163	“Bayesian statistics is a mathematical procedure that applies probabilities to statistical problems. It provides people the tools to update their beliefs in the evidence of new data.”
C164	The least squares criterion is a formula used to measure the accuracy of a straight line in depicting the data that was used to generate it. This mathematical formula is used to predict the behavior of the dependent variables. The approach is also called the least squares regression line.
C165	In unsupervised learning, an AI system is presented with unlabeled, uncategorized data and the system's algorithms act on the data without prior training. The output is dependent upon the coded algorithms. Subjecting a system to unsupervised learning is an established way of testing the capabilities of that system.
C166	Currently there are three classes of TCP/IP networks. Each class uses the 32-bit IP address space differently, providing more or fewer bits for the network part of the address. These classes are class A, class B, and class C.
C167	Linear Regression is a machine learning algorithm based on supervised learning. Linear regression performs the task to predict a dependent variable value (y) based on a given independent variable (x). So, this regression technique finds out a linear relationship between x (input) and y(output).
C168	Real numbers consist of zero (0), the positive and negative integers (-3, -1, 2, 4), and all the fractional and decimal values in between (0.4, 3.1415927, 1/2). Real numbers are divided into rational and irrational numbers.
C169	Kalman Filter works on prediction-correction model used for linear and time-variant or time-invariant systems. Prediction model involves the actual system and the process noise . Kalman gain is calculated based on RLS algorithm in order to reach the optimal value within less amount of time.
C170	49:131:21:31Suggested clip · 118 secondsLinear Algebra for Beginners \| Linear algebra for machine learning YouTubeStart of suggested clipEnd of suggested clip
C171	In the simplest sense, if something is “quantized”, that means it can only take on certain specific values, rather than a continuous range of values. For example, the energy that an electron can have when it's bound to an atom is quantized.
C172	For a multiplicative decomposition, this is done by dividing the series by the trend values. Next, seasonal factors are estimated using the de-trended series. For monthly data, this entails estimating an effect for each month of the year. For quarterly data, this entails estimating an effect for each quarter.
C173	A statistical test provides a mechanism for making quantitative decisions about a process or processes. The intent is to determine whether there is enough evidence to "reject" a conjecture or hypothesis about the process. A classic use of a statistical test occurs in process control studies.
C174	Chernoff faces, invented by Herman Chernoff in 1973, display multivariate data in the shape of a human face. The individual parts, such as eyes, ears, mouth and nose represent values of the variables by their shape, size, placement and orientation.
C175	Statistical data analysis. Finding structure in data and making predictions are the most important steps in Data Science. Here, in particular, statistical methods are essential since they are able to handle many different analytical tasks. Questions arising in data driven problems can often be translated to hypotheses.
C176	The difference between factor analysis and principal component analysis. Factor analysis explicitly assumes the existence of latent factors underlying the observed data. PCA instead seeks to identify variables that are composites of the observed variables.
C177	The sampling distribution of the sample mean can be thought of as "For a sample of size n, the sample mean will behave according to this distribution." Any random draw from that sampling distribution would be interpreted as the mean of a sample of n observations from the original population.
C178	Recurrent neural networks (RNN) are the state of the art algorithm for sequential data and are used by Apple's Siri and and Google's voice search. It is the first algorithm that remembers its input, due to an internal memory, which makes it perfectly suited for machine learning problems that involve sequential data.
C179	The three main metrics used to evaluate a classification model are accuracy, precision, and recall. Accuracy is defined as the percentage of correct predictions for the test data. It can be calculated easily by dividing the number of correct predictions by the number of total predictions.
C180	First, Cross-entropy (or softmax loss, but cross-entropy works better) is a better measure than MSE for classification, because the decision boundary in a classification task is large (in comparison with regression). For regression problems, you would almost always use the MSE.
C181	Lag sequential analysis is a method for analyzing the sequential dependency in a serially sequenced series of dichotomous codes representing different system states. The analysis assumes that the events are sequenced in time (a time series) but does not assume equal time intervals between events.
C182	The major difference between using a Z score and a T statistic is that you have to estimate the population standard deviation. The T test is also used if you have a small sample size (less than 30).
C183	Interpreting. If skewness is positive, the data are positively skewed or skewed right, meaning that the right tail of the distribution is longer than the left. If skewness is negative, the data are negatively skewed or skewed left, meaning that the left tail is longer.
C184	Semi-structured data is information that doesn't reside in a relational database but that does have some organizational properties that make it easier to analyze. Examples of semi-structured : CSV but XML and JSON documents are semi structured documents, NoSQL databases are considered as semi structured.
C185	The universe is considered an isolated system because the energy of the universe is constant. This matches with the definition of an isolated system, which is that energy is not exchanged with the surroundings, thus staying constant.
C186	A linear regression line has an equation of the form Y = a + bX, where X is the explanatory variable and Y is the dependent variable. The slope of the line is b, and a is the intercept (the value of y when x = 0).
C187	Definition. The term concept learning is originated in psychology, where it refers to the human ability to learn categories for object and to recognize new instances of those categories.
C188	Deviance is a measure of error; lower deviance means better fit to data. The greater the deviance, the worse the model fits compared to the best case (saturated). Deviance is a quality-of-fit statistic for a model that is often used for statistical hypothesis testing.
C189	This means when calculating the output of a node, the inputs are multiplied by weights, and a bias value is added to the result. The bias value allows the activation function to be shifted to the left or right, to better fit the data. You can think of the bias as a measure of how easy it is to get a node to fire.
C190	Troubleshoot steps for Deep Learning Start with something simple and make changes incrementally. Model optimizations like regularization can always wait after the code is debugged. Focus on verifying the model is functioning first. Set the regularization factors to zero.
C191	Answer: A variable is a datatype whose value can not be fixed. It can be change based on other parameters. For example, Let X is a variable so that its value can be anything like 1,2,3 or a,p,r, or any word. It can not be fixed.
C192	On this site, we use the normal distribution when the population standard deviation is known and the sample size is large. We might use either distribution when standard deviation is unknown and the sample size is very large.
C193	Bayesian analysis is a statistical paradigm that answers research questions about unknown parameters using probability statements.
C194	The simplest example of a non-linear operator (non-linear functional) is a real-valued function of a real argument other than a linear function. Under other restrictions on K(t,s,u) an Urysohn operator acts on other spaces, for instance, L2[a,b] or maps one Orlicz space LM1[a,b] into another LM2[a,b].
C195	t-test
C196	The receptive field is defined by the filter size of a layer within a convolution neural network. The receptive field is also an indication of the extent of the scope of input data a neuron or unit within a layer can be exposed to (see image below).
C197	The relationship between two variables is generally considered strong when their r value is larger than 0.7. The correlation r measures the strength of the linear relationship between two quantitative variables.
C198	marginal homogeneity
C199	The logit is a transformation. Logistic regression is a regression model. The logit transformation transforms a line to a logistic curve. Logistic regression fits a logistic curve to set of data where the dependent variable can only take the values 0 and 1.

C100

Most data can be categorized into 4 basic types from a Machine Learning perspective: numerical data, categorical data, time-series data, and text.

C101

It's a method of evaluating how well specific algorithm models the given data. If predictions deviates too much from actual results, loss function would cough up a very large number. Gradually, with the help of some optimization function, loss function learns to reduce the error in prediction.

C102

Momentum [1] or SGD with momentum is method which helps accelerate gradients vectors in the right directions, thus leading to faster converging. It is one of the most popular optimization algorithms and many state-of-the-art models are trained using it.

C103

In machine learning and statistics, the learning rate is a tuning parameter in an optimization algorithm that determines the step size at each iteration while moving toward a minimum of a loss function.

C104

Heterogeneity in statistics means that your populations, samples or results are different. It is the opposite of homogeneity, which means that the population/data/results are the same.

C105

Q-learning is an off policy reinforcement learning algorithm that seeks to find the best action to take given the current state. It's considered off-policy because the q-learning function learns from actions that are outside the current policy, like taking random actions, and therefore a policy isn't needed.

C106

Validation set is different from test set. Validation set actually can be regarded as a part of training set, because it is used to build your model, neural networks or others. It is usually used for parameter selection and to avoild overfitting. Test set is used for performance evaluation.

C107

The F-distribution, also known Fisher-Snedecor distribution is extensively used to test for equality of variances from two normal populations. F-distribution got its name after R.A. Fisher who initially developed this concept in 1920s. It is a probability distribution of an F-statistic.

C108

The disadvantages are numerous. Cross-over studies are often of longer duration than parallel-group studies. There may be difficulty in incorporating multiple dosage arms and in dealing with drop-outs; patients who only complete the first evaluation phase contribute little to the analysis.

C109

Gradient boosting is a machine learning technique for regression and classification problems, which produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees. (

C110

The intuition is simple projection. This picture is from wiki. is the observed response and we predict by the linear combination of the explanatory variables which in inside the vector space .

C111

Much of the modern innovations in image recognition is reliant on Deep Learning technology, an advanced type of Machine Learning, and the modern wonder of Artificial Intelligence. For image recognition, the kind of neural network used is called convolutional neural networks.

C112

From Wikipedia, the free encyclopedia. In statistics and signal processing, a minimum mean square error (MMSE) estimator is an estimation method which minimizes the mean square error (MSE), which is a common measure of estimator quality, of the fitted values of a dependent variable.

C113

Frequency distribution in statistics provides the information of the number of occurrences (frequency) of distinct values distributed within a given period of time or interval, in a list, table, or graphical representation. Grouped and Ungrouped are two types of Frequency Distribution.

C114

In order to fit the best intercept line between the points in the above scatter plots, we use a metric called “Sum of Squared Errors” (SSE) and compare the lines to find out the best fit by reducing errors.

C115

To find the interquartile range (IQR), first find the median (middle value) of the lower and upper half of the data. These values are quartile 1 (Q1) and quartile 3 (Q3). The IQR is the difference between Q3 and Q1.

C116

Data is a set of qualitative or quantitative variables – it can be structured or unstructured, machine readable or not, digital or analogue, personal or not. There are “dimensions” that distinguish data from BIG DATA, summarised as the “3 Vs” of data: Volume, Variety, Velocity. Hence, BIG DATA, is not just “more” data.

C117

A decision tree is one of the supervised machine learning algorithms. This algorithm can be used for regression and classification problems — yet, is mostly used for classification problems. A decision tree follows a set of if-else conditions to visualize the data and classify it according to the conditions.

C118

The logarithm is to exponentiation as division is to multiplication: The logarithm is the inverse of the exponent: it undoes exponentiation. When studying logarithms, always remember the following fundamental equivalence: if and only if . Whenever one of these is true, so is the other.

C119

A convolutional stage in a neural network ensures that each part of the neural network has essentially the same edge detector. So even if your data are biased to have edges only in, say, the lower left side of the image set, your connection weights will not reflect this systematic bias.

C120

Normal distribution, also known as the Gaussian distribution, is a probability distribution that is symmetric about the mean, showing that data near the mean are more frequent in occurrence than data far from the mean. In graph form, normal distribution will appear as a bell curve.

C121

The AUC value lies between 0.5 to 1 where 0.5 denotes a bad classifer and 1 denotes an excellent classifier.

C122

Word2Vec takes texts as training data for a neural network. The resulting embedding captures whether words appear in similar contexts. GloVe focuses on words co-occurrences over the whole corpus. Its embeddings relate to the probabilities that two words appear together.

C123

Simply put, an activation function is a function that is added into an artificial neural network in order to help the network learn complex patterns in the data. When comparing with a neuron-based model that is in our brains, the activation function is at the end deciding what is to be fired to the next neuron.

C124

I often draw a distinction between exploratory and explanatory data analysis. Exploratory analysis is what you do to get familiar with the data. Explanatory analysis is what happens when you have something specific you want to show an audience - probably about those 1 or 2 precious gemstones.

C125

1a(1) : of, relating to, resembling, or having a graph that is a line and especially a straight line : straight. (2) : involving a single dimension.

C126

It is easy to check that the MLE is an unbiased estimator (E[̂θMLE(y)] = θ). To determine the CRLB, we need to calculate the Fisher information of the model.

C127

There are two groups of metrics that may be useful for imbalanced classification because they focus on one class; they are sensitivity-specificity and precision-recall.

C128

AUC stands for "Area under the ROC Curve." That is, AUC measures the entire two-dimensional area underneath the entire ROC curve (think integral calculus) from (0,0) to (1,1). Figure 5. AUC (Area under the ROC Curve).

C129

A statistic T = r(X1,X2,··· ,Xn) is a sufficient statistic if for each t, the conditional distribution of X1,X2, ···,Xn given T = t and θ does not depend on θ.

C130

1:314:30Suggested clip · 120 secondsCumulative Frequency Distribution (Less than and More than YouTubeStart of suggested clipEnd of suggested clip

C131

How to train a Machine Learning model in 5 minutesModel Naming — Give Your Model a Name: Let's start with giving your model a name, describe your model and attach tags to your model. Data Type Selection — Choose data type(Images/Text/CSV): It's time to tell us about the type of data you want to train your model.More items

C132

Manipulate data using Excel or Google Sheets. This may include plotting the data out, creating pivot tables, and so on. Analyze and interpret the data using statistical tools (i.e. finding correlations, trends, outliers, etc.). Present this data in meaningful ways: graphs, visualizations, charts, tables, etc.

C133

The second reason you may see validation loss lower than training loss is due to how the loss value are measured and reported: Training loss is measured during each epoch. While validation loss is measured after each epoch.

C134

Data wrangling is the process of gathering, selecting, and transforming data to answer an analytical question. Also known as data cleaning or “munging”, legend has it that this wrangling costs analytics professionals as much as 80% of their time, leaving only 20% for exploration and modeling.

C135

The chi-square statistic can never be negative. What does it mean to obtain a negative value for the chi-square statistic? the null hypothesis is rejected if the observed U is less than or equal to the critical U.

C136

Fisher's exact test obtains its two-tailed P value by computing the probabilities associated with all possible tables that have the same row and column totals. Then, it identifies the alternative tables with a probability that is less than that of the observed table.

C137

International communication (also referred to as the study of global communication or transnational communication) is the communication practice that occurs across international borders. International communication "encompasses political, economic, social, cultural and military concerns".

C138

Artificial neural networks use backpropagation as a learning algorithm to compute a gradient descent with respect to weights. Because backpropagation requires a known, desired output for each input value in order to calculate the loss function gradient, it is usually classified as a type of supervised machine learning.

C139

In most mechanical systems or models, you can determine the degrees of freedom using the following formula:DOF = 6 x (number of bodies not including ground) – constraints.DOF = (6 x 1) – (2 x 5)DOF = 6 x (number of bodies not including ground) – constraints + redundancies.1 = (6 x 1) – 10 + redundancies.More items

C140

Statistical Package for the Social Sciences

C141

In the “Compute Variable” dialog box that opens, enter a name for the new centered variable in the “Target Variable:” text box at the top right. In the “Numeric Expression:” box, write “math-52.65” as shown in Figure 2. Press OK to create the centered variable.

C142

Summary. The probability distribution of a discrete random variable X is a listing of each possible value x taken by X along with the probability P(x) that X takes that value in one trial of the experiment.

C143

Grid-searching is the process of scanning the data to configure optimal parameters for a given model. Grid-searching can be applied across machine learning to calculate the best parameters to use for any given model.

C144

The definition of conditional probability can be rewritten as: P(E ∩F) = P(E|F)P(F) which we call the Chain Rule. Intuitively it states that the probability of observing events E and F is the. probability of observing F, multiplied by the probability of observing E, given that you have observed F.

C145

According to the (Research Methods for Business Students) book, to assess the relationship between two ordinal variables is by using Spearman's rank correlation coefficient (Spearman's rho) or Kendall's rank-order correlation coefficient (Kendall's tau).

C146

SVM or Support Vector Machine is a linear model for classification and regression problems. It can solve linear and non-linear problems and work well for many practical problems. The idea of SVM is simple: The algorithm creates a line or a hyperplane which separates the data into classes.

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The general regression tree building methodology allows input variables to be a mixture of continuous and categorical variables. A decision tree is generated when each decision node in the tree contains a test on some input variable's value. The terminal nodes of the tree contain the predicted output variable values.

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Properties of a normal distributionThe mean, mode and median are all equal.The curve is symmetric at the center (i.e. around the mean, μ).Exactly half of the values are to the left of center and exactly half the values are to the right.The total area under the curve is 1.

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Traditional machine learning methods such as Naïve Bayes, Logistic Regression and Support Vector Machines (SVM) are widely used for large-scale sentiment analysis because they scale well.

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There are three basic concepts in reinforcement learning: state, action, and reward. The state describes the current situation. For a robot that is learning to walk, the state is the position of its two legs. For a Go program, the state is the positions of all the pieces on the board.

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Cohen's kappa measures the agreement between two raters who each classify N items into C mutually exclusive categories.¹ A simple way to think this is that Cohen's Kappa is a quantitative measure of reliability for two raters that are rating the same thing, corrected for how often that the raters may agree by chance.

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Similarity is the measure of how much alike two data objects are. Similarity in a data mining context is usually described as a distance with dimensions representing features of the objects. A small distance indicating a high degree of similarity and a large distance indicating a low degree of similarity.

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From implementation point of view, Huffman coding is easier than arithmetic coding. Arithmetic algorithm yields much more compression ratio than Huffman algorithm while Huffman coding needs less execution time than the arithmetic coding.

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Conditional probability is the probability of one event occurring with some relationship to one or more other events. For example: Event A is that it is raining outside, and it has a 0.3 (30%) chance of raining today. Event B is that you will need to go outside, and that has a probability of 0.5 (50%).

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Naïve Bayes has a naive assumption of conditional independence for every feature, which means that the algorithm expects the features to be independent which not always is the case. Logistic regression is a linear classification method that learns the probability of a sample belonging to a certain class.

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A false positive, also known as Type I error or alpha error, is an error that occurs when a researcher falsely concludes that an effect exists, or when a null hypothesis is rejected even though the null is true. On the basis of these data, the researcher concludes that there is an effect.

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Association Rule Mining, as the name suggests, association rules are simple If/Then statements that help discover relationships between seemingly independent relational databases or other data repositories. Most machine learning algorithms work with numeric datasets and hence tend to be mathematical.

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Need for Batch Consumption From Kafka Data ingestion system are built around Kafka. They are followed by lambda architectures with separate pipelines for real-time stream processing and batch processing. Real-time stream processing pipelines are facilitated by Spark Streaming, Flink, Samza, Storm, etc.

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A variable is a symbol that represents some quantity. A random variable is a value that follows some probability distribution. In other words, it's a value that is subject to some randomness or chance.

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To measure the relationship between numeric variable and categorical variable with > 2 levels you should use eta correlation (square root of the R2 of the multifactorial regression). If the categorical variable has 2 levels, point-biserial correlation is used (equivalent to the Pearson correlation).

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With binary data the variance is a function of the mean, and in particular is not constant as the mean changes. This violates one of the standard linear regression assumptions that the variance of the residual errors is constant.

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Abstract: In the big data era, the data are generated from different sources or observed from different views. These data are referred to as multi-view data. Multi-view Clustering (MvC) has attracted increasing attention in recent years by aiming to exploit complementary and consensus information across multiple views.

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“Bayesian statistics is a mathematical procedure that applies probabilities to statistical problems. It provides people the tools to update their beliefs in the evidence of new data.”

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The least squares criterion is a formula used to measure the accuracy of a straight line in depicting the data that was used to generate it. This mathematical formula is used to predict the behavior of the dependent variables. The approach is also called the least squares regression line.

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In unsupervised learning, an AI system is presented with unlabeled, uncategorized data and the system's algorithms act on the data without prior training. The output is dependent upon the coded algorithms. Subjecting a system to unsupervised learning is an established way of testing the capabilities of that system.

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Currently there are three classes of TCP/IP networks. Each class uses the 32-bit IP address space differently, providing more or fewer bits for the network part of the address. These classes are class A, class B, and class C.

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Linear Regression is a machine learning algorithm based on supervised learning. Linear regression performs the task to predict a dependent variable value (y) based on a given independent variable (x). So, this regression technique finds out a linear relationship between x (input) and y(output).

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Real numbers consist of zero (0), the positive and negative integers (-3, -1, 2, 4), and all the fractional and decimal values in between (0.4, 3.1415927, 1/2). Real numbers are divided into rational and irrational numbers.

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Kalman Filter works on prediction-correction model used for linear and time-variant or time-invariant systems. Prediction model involves the actual system and the process noise . Kalman gain is calculated based on RLS algorithm in order to reach the optimal value within less amount of time.

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49:131:21:31Suggested clip · 118 secondsLinear Algebra for Beginners | Linear algebra for machine learning YouTubeStart of suggested clipEnd of suggested clip

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In the simplest sense, if something is “quantized”, that means it can only take on certain specific values, rather than a continuous range of values. For example, the energy that an electron can have when it's bound to an atom is quantized.

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For a multiplicative decomposition, this is done by dividing the series by the trend values. Next, seasonal factors are estimated using the de-trended series. For monthly data, this entails estimating an effect for each month of the year. For quarterly data, this entails estimating an effect for each quarter.

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A statistical test provides a mechanism for making quantitative decisions about a process or processes. The intent is to determine whether there is enough evidence to "reject" a conjecture or hypothesis about the process. A classic use of a statistical test occurs in process control studies.

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Chernoff faces, invented by Herman Chernoff in 1973, display multivariate data in the shape of a human face. The individual parts, such as eyes, ears, mouth and nose represent values of the variables by their shape, size, placement and orientation.

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Statistical data analysis. Finding structure in data and making predictions are the most important steps in Data Science. Here, in particular, statistical methods are essential since they are able to handle many different analytical tasks. Questions arising in data driven problems can often be translated to hypotheses.

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The difference between factor analysis and principal component analysis. Factor analysis explicitly assumes the existence of latent factors underlying the observed data. PCA instead seeks to identify variables that are composites of the observed variables.

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The sampling distribution of the sample mean can be thought of as "For a sample of size n, the sample mean will behave according to this distribution." Any random draw from that sampling distribution would be interpreted as the mean of a sample of n observations from the original population.

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Recurrent neural networks (RNN) are the state of the art algorithm for sequential data and are used by Apple's Siri and and Google's voice search. It is the first algorithm that remembers its input, due to an internal memory, which makes it perfectly suited for machine learning problems that involve sequential data.

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The three main metrics used to evaluate a classification model are accuracy, precision, and recall. Accuracy is defined as the percentage of correct predictions for the test data. It can be calculated easily by dividing the number of correct predictions by the number of total predictions.

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First, Cross-entropy (or softmax loss, but cross-entropy works better) is a better measure than MSE for classification, because the decision boundary in a classification task is large (in comparison with regression). For regression problems, you would almost always use the MSE.

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Lag sequential analysis is a method for analyzing the sequential dependency in a serially sequenced series of dichotomous codes representing different system states. The analysis assumes that the events are sequenced in time (a time series) but does not assume equal time intervals between events.

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The major difference between using a Z score and a T statistic is that you have to estimate the population standard deviation. The T test is also used if you have a small sample size (less than 30).

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Interpreting. If skewness is positive, the data are positively skewed or skewed right, meaning that the right tail of the distribution is longer than the left. If skewness is negative, the data are negatively skewed or skewed left, meaning that the left tail is longer.

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Semi-structured data is information that doesn't reside in a relational database but that does have some organizational properties that make it easier to analyze. Examples of semi-structured : CSV but XML and JSON documents are semi structured documents, NoSQL databases are considered as semi structured.

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The universe is considered an isolated system because the energy of the universe is constant. This matches with the definition of an isolated system, which is that energy is not exchanged with the surroundings, thus staying constant.

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A linear regression line has an equation of the form Y = a + bX, where X is the explanatory variable and Y is the dependent variable. The slope of the line is b, and a is the intercept (the value of y when x = 0).

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Definition. The term concept learning is originated in psychology, where it refers to the human ability to learn categories for object and to recognize new instances of those categories.

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Deviance is a measure of error; lower deviance means better fit to data. The greater the deviance, the worse the model fits compared to the best case (saturated). Deviance is a quality-of-fit statistic for a model that is often used for statistical hypothesis testing.

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This means when calculating the output of a node, the inputs are multiplied by weights, and a bias value is added to the result. The bias value allows the activation function to be shifted to the left or right, to better fit the data. You can think of the bias as a measure of how easy it is to get a node to fire.

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Troubleshoot steps for Deep Learning Start with something simple and make changes incrementally. Model optimizations like regularization can always wait after the code is debugged. Focus on verifying the model is functioning first. Set the regularization factors to zero.

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Answer: A variable is a datatype whose value can not be fixed. It can be change based on other parameters. For example, Let X is a variable so that its value can be anything like 1,2,3 or a,p,r, or any word. It can not be fixed.

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On this site, we use the normal distribution when the population standard deviation is known and the sample size is large. We might use either distribution when standard deviation is unknown and the sample size is very large.

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Bayesian analysis is a statistical paradigm that answers research questions about unknown parameters using probability statements.

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The simplest example of a non-linear operator (non-linear functional) is a real-valued function of a real argument other than a linear function. Under other restrictions on K(t,s,u) an Urysohn operator acts on other spaces, for instance, L2[a,b] or maps one Orlicz space LM1[a,b] into another LM2[a,b].

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t-test

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The receptive field is defined by the filter size of a layer within a convolution neural network. The receptive field is also an indication of the extent of the scope of input data a neuron or unit within a layer can be exposed to (see image below).

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The relationship between two variables is generally considered strong when their r value is larger than 0.7. The correlation r measures the strength of the linear relationship between two quantitative variables.

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marginal homogeneity

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The logit is a transformation. Logistic regression is a regression model. The logit transformation transforms a line to a logistic curve. Logistic regression fits a logistic curve to set of data where the dependent variable can only take the values 0 and 1.