mteb/MLQuestions · Datasets at Hugging Face

_id stringlengths 2 6	text stringlengths 3 395	title stringclasses 1 value
C10900	How to Use K-means Cluster Algorithms in Predictive AnalysisPick k random items from the dataset and label them as cluster representatives.Associate each remaining item in the dataset with the nearest cluster representative, using a Euclidean distance calculated by a similarity function.Recalculate the new clusters' representatives.More items
C10901	Events are dependent if the outcome of one event affects the outcome of another. For example, if you draw two colored balls from a bag and the first ball is not replaced before you draw the second ball then the outcome of the second draw will be affected by the outcome of the first draw.
C10902	Systematic sampling is a type of probability sampling method in which sample members from a larger population are selected according to a random starting point but with a fixed, periodic interval. This interval, called the sampling interval, is calculated by dividing the population size by the desired sample size.
C10903	So unlike biological neurons, artificial neurons don't just “fire”: they send continuous values instead of binary signals. Depending on their activation functions, they might somewhat fire all the time, but the strength of these signals varies.
C10904	Any point (x) from a normal distribution can be converted to the standard normal distribution (z) with the formula z = (x-mean) / standard deviation. z for any particular x value shows how many standard deviations x is away from the mean for all x values.
C10905	To overcome the issue of the curse of dimensionality, Dimensionality Reduction is used to reduce the feature space with consideration by a set of principal features.
C10906	cortex
C10907	Feature extraction is a process of dimensionality reduction by which an initial set of raw data is reduced to more manageable groups for processing. A characteristic of these large data sets is a large number of variables that require a lot of computing resources to process.
C10908	Descriptive Analytics tells you what happened in the past. Predictive Analytics predicts what is most likely to happen in the future. Prescriptive Analytics recommends actions you can take to affect those outcomes.
C10909	KMeans is a clustering algorithm which divides observations into k clusters. Since we can dictate the amount of clusters, it can be easily used in classification where we divide data into clusters which can be equal to or more than the number of classes.
C10910	The relationship between margin of error and sample size is simple: As the sample size increases, the margin of error decreases. If you think about it, it makes sense that the more information you have, the more accurate your results are going to be (in other words, the smaller your margin of error will get).
C10911	In particular, a random experiment is a process by which we observe something uncertain. After the experiment, the result of the random experiment is known. An outcome is a result of a random experiment. The set of all possible outcomes is called the sample space.
C10912	Dummy variables are useful because they enable us to use a single regression equation to represent multiple groups. This means that we don't need to write out separate equation models for each subgroup. The dummy variables act like 'switches' that turn various parameters on and off in an equation.
C10913	How to Calculate a Confusion MatrixYou need a test dataset or a validation dataset with expected outcome values.Make a prediction for each row in your test dataset.From the expected outcomes and predictions count: The number of correct predictions for each class.
C10914	This implies that bias and variance of an estimator are complementary to each other i.e. an estimator with high bias will vary less(have low variance) and an estimator with high variance will have less bias(as it can vary more to fit/explain/estimate the data points).
C10915	Chebyshev's inequality, also known as Chebyshev's theorem, is a statistical tool that measures dispersion in a data population. The theorem states that no more than 1 / k2 of the distribution's values will be more than k standard deviations away from the mean.
C10916	Optimization is the most essential ingredient in the recipe of machine learning algorithms. It starts with defining some kind of loss function/cost function and ends with minimizing the it using one or the other optimization routine.
C10917	We can use the median with the interquartile range, or we can use the mean with the standard deviation.
C10918	Why gradient clipping accelerates training: A theoretical justification for adaptivity. These observations motivate us to introduce a novel relaxation of gradient smoothness that is weaker than the commonly used Lipschitz smoothness assumption.
C10919	As the formula shows, the standard score is simply the score, minus the mean score, divided by the standard deviation. Therefore, let's return to our two questions.
C10920	Learning of probability helps you in making informed decisions about likelihood of events, based on a pattern of collected data. In the context of data science, statistical inferences are often used to analyze or predict trends from data, and these inferences use probability distributions of data.
C10921	The decision tree splits the nodes on all available variables and then selects the split which results in most homogeneous sub-nodes. The ID3 algorithm builds decision trees using a top-down greedy search approach through the space of possible branches with no backtracking.
C10922	Sigmoid function, unlike step function, introduces non-linearity into our neural network model. This non-linear activation function, when used by each neuron in a multi-layer neural network, produces a new “representation” of the original data, and ultimately allows for non-linear decision boundary, such as XOR.
C10923	Examples of multivariate regression Example 2. A doctor has collected data on cholesterol, blood pressure, and weight. She also collected data on the eating habits of the subjects (e.g., how many ounces of red meat, fish, dairy products, and chocolate consumed per week).
C10924	The false discovery rate (FDR) is a statistical approach used in multiple hypothesis testing to correct for multiple comparisons. The FDR is defined as the expected proportion of false discoveries, i.e., incorrectly rejected null hypothesis, among all discoveries (Benjamini and Hochberg 1995).
C10925	A simple random sample is similar to a random sample. The difference between the two is that with a simple random sample, each object in the population has an equal chance of being chosen. With random sampling, each object does not necessarily have an equal chance of being chosen.
C10926	The other major way to estimate inter-rater reliability is appropriate when the measure is a continuous one. There, all you need to do is calculate the correlation between the ratings of the two observers. For instance, they might be rating the overall level of activity in a classroom on a 1-to-7 scale.
C10927	With cluster sampling, the researcher divides the population into separate groups, called clusters. Then, a simple random sample of clusters is selected from the population. For example, given equal sample sizes, cluster sampling usually provides less precision than either simple random sampling or stratified sampling.
C10928	A high pass filter can be formed by placing a capacitor in series with an inverting gain stage as shown in Figure 11.13.
C10929	Stochastic Gradient Descent: you would randomly select one of those training samples at each iteration to update your coefficients. Online Gradient Descent: you would use the "most recent" sample at each iteration. There is no stochasticity as you deterministically select your sample.
C10930	The clarity of visual features are excellent inputs to Deep Learning models. Because images can learn from themselves in a semi-supervised manner, there is no data required.
C10931	In short, fourier series is for periodic signals and fourier transform is for aperiodic signals. Fourier series is used to decompose signals into basis elements (complex exponentials) while fourier transforms are used to analyze signal in another domain (e.g. from time to frequency, or vice versa).
C10932	In statistics, the residual sum of squares (RSS), also known as the sum of squared residuals (SSR) or the sum of squared estimate of errors (SSE), is the sum of the squares of residuals (deviations predicted from actual empirical values of data).
C10933	Whereas the null hypothesis of the two-sample t test is equal means, the null hypothesis of the Wilcoxon test is usually taken as equal medians. Another way to think of the null is that the two populations have the same distribution with the same median.
C10934	To find the critical value, follow these steps.Compute alpha (α): α = 1 - (confidence level / 100)Find the critical probability (p): p = 1 - α/2.To express the critical value as a z-score, find the z-score having a cumulative probability equal to the critical probability (p*).More items
C10935	Statistically Valid Sample Size Criteria Probability or percentage: The percentage of people you expect to respond to your survey or campaign. Confidence: How confident you need to be that your data is accurate. Expressed as a percentage, the typical value is 95% or 0.95.
C10936	Click on the triangle-shaped icon located at the top right corner of the panel, and then choose "Save Path". Next, select "Clipping Path" from the same drop-down menu. A new dialog box will appear with a variety of clipping path settings. Make sure your path is selected, and then click OK.
C10937	Related calculationsFalse positive rate (α) = type I error = 1 − specificity = FP / (FP + TN) = 180 / (180 + 1820) = 9%False negative rate (β) = type II error = 1 − sensitivity = FN / (TP + FN) = 10 / (20 + 10) = 33%Power = sensitivity = 1 − βLisää kohteita…
C10938	To measure test-retest reliability, you conduct the same test on the same group of people at two different points in time. Then you calculate the correlation between the two sets of results.
C10939	In probability theory and statistics, the beta distribution is a family of continuous probability distributions defined on the interval [0, 1] parameterized by two positive shape parameters, denoted by α and β, that appear as exponents of the random variable and control the shape of the distribution.
C10940	An association rule has two parts: an antecedent (if) and a consequent (then). An antecedent is an item found within the data. Support is an indication of how frequently the items appear in the data. Confidence indicates the number of times the if-then statements are found true.
C10941	Inductive Learning is where we are given examples of a function in the form of data (x) and the output of the function (f(x)). The goal of inductive learning is to learn the function for new data (x). Classification: when the function being learned is discrete. Regression: when the function being learned is continuous.
C10942	There are four main types of probability sample.Simple random sampling. In a simple random sample, every member of the population has an equal chance of being selected. Systematic sampling. Stratified sampling. Cluster sampling.
C10943	receiver operating characteristic curve
C10944	The easiest[A] way to evaluate the actual value of a Tensor object is to pass it to the Session. run() method, or call Tensor. eval() when you have a default session (i.e. in a with tf. Session(): block, or see below).
C10945	To find the mean absolute deviation of the data, start by finding the mean of the data set. Find the sum of the data values, and divide the sum by the number of data values. Find the absolute value of the difference between each data value and the mean: \|data value – mean\|.
C10946	To address this issue, there is a modification to Cohen's kappa called weighted Cohen's kappa. The weighted kappa is calculated using a predefined table of weights which measure the degree of disagreement between the two raters, the higher the disagreement the higher the weight.
C10947	NAT (Network Address Translation) is a feature of the Firewall Software Blade and replaces IPv4 and IPv6 addresses to add more security. You can enable NAT for all SmartDashboard objects to help manage network traffic. NAT protects the identity of a network and does not show internal IP addresses to the Internet.
C10948	An F-test (Snedecor and Cochran, 1983) is used to test if the variances of two populations are equal. This test can be a two-tailed test or a one-tailed test. The two-tailed version tests against the alternative that the variances are not equal.
C10949	Multinomial logistic regression (often just called 'multinomial regression') is used to predict a nominal dependent variable given one or more independent variables. It is sometimes considered an extension of binomial logistic regression to allow for a dependent variable with more than two categories.
C10950	Nonparametric statistics refers to a statistical method in which the data are not assumed to come from prescribed models that are determined by a small number of parameters; examples of such models include the normal distribution model and the linear regression model.
C10951	What Are Moments in Statistics?Moments About the MeanFirst, calculate the mean of the values.Next, subtract this mean from each value.Then raise each of these differences to the sth power.Now add the numbers from step #3 together.Finally, divide this sum by the number of values we started with.
C10952	Random assignment is however a process of randomly assigning subjects to experimental or control groups. This is a standard practice in true experimental research to ensure that treatment groups are similar (equivalent) to each other and to the control group, prior to treatment administration.
C10953	A quantile defines a particular part of a data set, i.e. a quantile determines how many values in a distribution are above or below a certain limit. Special quantiles are the quartile (quarter), the quintile (fifth) and percentiles (hundredth).
C10954	A classification is an ordered set of related categories used to group data according to its similarities. It consists of codes and descriptors and allows survey responses to be put into meaningful categories in order to produce useful data. A classification is a useful tool for anyone developing statistical surveys.
C10955	6 Practices to enhance the performance of a Text ClassificationDomain Specific Features in the Corpus. For a classification problem, it is important to choose the test and training corpus very carefully. Use An Exhaustive Stopword List. Noise Free Corpus. Eliminating features with extremely low frequency. Normalized Corpus. Use Complex Features: n-grams and part of speech tags.
C10956	An eager algorithm executes immediately and returns a result. A lazy algorithm defers computation until it is necessary to execute and then produces a result. Eager algorithms are easier to understand and debug. They can also be highly optimized for a single use case (e.g. filter ).
C10957	In representation learning, features are extracted from unlabeled data by training a neural network on a secondary, supervised learning task. When applying deep learning to natural language processing (NLP) tasks, the model must simultaneously learn several language concepts: the meanings of words.
C10958	Data augmentation in data analysis are techniques used to increase the amount of data by adding slightly modified copies of already existing data or newly created synthetic data from existing data. It acts as a regularizer and helps reduce overfitting when training a machine learning model.
C10959	Artificial intelligence has close connections with philosophy because both use concepts that have the same names and these include intelligence, action, consciousness, epistemology, and even free will. These factors contributed to the emergence of the philosophy of artificial intelligence.
C10960	Conclusion. Cross-Validation is a very powerful tool. It helps us better use our data, and it gives us much more information about our algorithm performance. In complex machine learning models, it's sometimes easy not pay enough attention and use the same data in different steps of the pipeline.
C10961	Clustering is done based on a similarity measure to group similar data objects together. This similarity measure is most commonly and in most applications based on distance functions such as Euclidean distance, Manhattan distance, Minkowski distance, Cosine similarity, etc. to group objects in clusters.
C10962	A composite hypothesis test contains more than one parameter and more than one model. In a simple hypothesis test, the probability density functions for both the null hypothesis (H0) and alternate hypothesis (H1) are known.
C10963	Receptive field, region in the sensory periphery within which stimuli can influence the electrical activity of sensory cells.
C10964	The cumulative density function gives you the probability of a random variable being on or below a certain value. The quantile function is the opposite of that. i.e. you give it a probability and it tells you the random variable value. A quartile is the value of the quantile at the probabilities 0.25, 0.5 and 0.75.
C10965	In general, if the data is normally distributed, parametric tests should be used. If the data is non-normal, non-parametric tests should be used.
C10966	So when you perform t-test for comparison of two means or ANOVA forr comparison of multiple means. You need dummy variables. In your case if the data is categorical you'll definitely need to convert them so simultaneously they are becoming dummy by themselves. Hence YES, you can use these tests for categorical data.
C10967	Server clustering refers to a group of servers working together on one system to provide users with higher availability. These clusters are used to reduce downtime and outages by allowing another server to take over in the event of an outage. Here's how it works. A group of servers are connected to a single system.
C10968	The shape of any distribution can be described by its various 'moments'. The first four are: 1) The mean, which indicates the central tendency of a distribution. 2) The second moment is the variance, which indicates the width or deviation.
C10969	A common cause of sampling bias lies in the design of the study or in the data collection procedure, both of which may favor or disfavor collecting data from certain classes or individuals or in certain conditions. Figure 1: Possible sources of bias occurring in the selection of a sample from a population.
C10970	12:3117:15Suggested clip · 120 secondsLogistic Regression in R, Clearly Explained!!!! - YouTubeYouTubeStart of suggested clipEnd of suggested clip
C10971	Dependent events: Two events are dependent when the outcome of the first event influences the outcome of the second event. The probability of two dependent events is the product of the probability of X and the probability of Y AFTER X occurs.
C10972	A bounding box is an imaginary rectangle that serves as a point of reference for object detection and creates a collision box for that object. Data annotators draw these rectangles over images, outlining the object of interest within each image by defining its X and Y coordinates.
C10973	The Least Squares Regression Line is the line that makes the vertical distance from the data points to the regression line as small as possible. It's called a “least squares” because the best line of fit is one that minimizes the variance (the sum of squares of the errors).
C10974	Bagging is a way to decrease the variance in the prediction by generating additional data for training from dataset using combinations with repetitions to produce multi-sets of the original data. Boosting is an iterative technique which adjusts the weight of an observation based on the last classification.
C10975	Response bias is a general term for a wide range of tendencies for participants to respond inaccurately or falsely to questions. These biases are prevalent in research involving participant self-report, such as structured interviews or surveys.
C10976	The standard deviation formula may look confusing, but it will make sense after we break it down. Step 1: Find the mean.Step 2: For each data point, find the square of its distance to the mean.Step 3: Sum the values from Step 2.Step 4: Divide by the number of data points.Step 5: Take the square root.
C10977	“Human error” is not a source of experimental error. You must classify specific errors as random or systematic and identify the source of the error. Human error cannot be stated as experimental error.
C10978	Binning is a way to group a number of more or less continuous values into a smaller number of "bins". For example, if you have data about a group of people, you might want to arrange their ages into a smaller number of age intervals.
C10979	Interpret the key results for Binary Logistic RegressionStep 1: Determine whether the association between the response and the term is statistically significant.Step 2: Understand the effects of the predictors.Step 3: Determine how well the model fits your data.Step 4: Determine whether the model does not fit the data.
C10980	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).
C10981	Bias in data can result from: survey questions that are constructed with a particular slant. choosing a known group with a particular background to respond to surveys. reporting data in misleading categorical groupings.
C10982	Tensorflow is the most famous library used in production for deep learning models. However TensorFlow is not that easy to use. On the other hand, Keras is a high level API built on TensorFlow (and can be used on top of Theano too). It is more user-friendly and easy to use as compared to TF.
C10983	If you are working on a classification problem, the best score is 100% accuracy. If you are working on a regression problem, the best score is 0.0 error. These scores are an impossible to achieve upper/lower bound.
C10984	Converting a Covariance Matrix to a Correlation Matrix First, use the DIAG function to extract the variances from the diagonal elements of the covariance matrix. Then invert the matrix to form the diagonal matrix with diagonal elements that are the reciprocals of the standard deviations.
C10985	In the context of neural networks, a perceptron is an artificial neuron using the Heaviside step function as the activation function. The perceptron algorithm is also termed the single-layer perceptron, to distinguish it from a multilayer perceptron, which is a misnomer for a more complicated neural network.
C10986	In the field of artificial intelligence, inference engine is a component of the system that applies logical rules to the knowledge base to deduce new information. The first inference engines were components of expert systems. The knowledge base stored facts about the world.
C10987	In fact, linear regression analysis works well, even with non-normal errors.
C10988	Let's take a look at some of the important business problems solved by machine learning.Manual data entry. Detecting Spam. Product recommendation. Medical Diagnosis. Customer segmentation and Lifetime value prediction. Financial analysis. Predictive maintenance. Image recognition (Computer Vision)
C10989	R is now used by over 50% of data miners. R, Python, and SQL were the most popular programming languages. Python, Lisp/Clojure, and Unix tools showest the highest growth in 2012, while Java and MATLAB slightly declined in popularity.
C10990	The four elements of a descriptive statistics problem include population/sample, tables/graphs, identifying patterns, and A. data.
C10991	The 7 Steps of Machine Learning1 - Data Collection. The quantity & quality of your data dictate how accurate our model is. 2 - Data Preparation. Wrangle data and prepare it for training. 3 - Choose a Model. 4 - Train the Model. 5 - Evaluate the Model. 6 - Parameter Tuning. 7 - Make Predictions.
C10992	The neuron is the basic working unit of the brain, a specialized cell designed to transmit information to other nerve cells, muscle, or gland cells. Neurons are cells within the nervous system that transmit information to other nerve cells, muscle, or gland cells. Most neurons have a cell body, an axon, and dendrites.
C10993	Taking the square root of the variance gives us the units used in the original scale and this is the standard deviation. Standard deviation is the measure of spread most commonly used in statistical practice when the mean is used to calculate central tendency. Thus, it measures spread around the mean.
C10994	Many time series show periodic behavior. This periodic behavior can be very complex. Spectral analysis is a technique that allows us to discover underlying periodicities. To perform spectral analysis, we first must transform data from time domain to frequency domain.
C10995	0:294:16Suggested clip · 116 secondsGeometric distribution moment generating function - YouTubeYouTubeStart of suggested clipEnd of suggested clip
C10996	In a multilevel model, we use random variables to model the variation between groups. An alternative approach is to use an ordinary regression model, but to include a set of dummy variables to represent the differences between the groups. The multilevel approach offers several advantages.
C10997	Quantum fields are matter. The simplest “practical” quantum field theory is quantum electromagnetism. In it, two fields exist: the electromagnetic field and the “electron field”. These two fields continuously interact with each other, energy and momentum are transferred, and excitations are created or destroyed.
C10998	Restricted Boltzmann Machines are used to analyze and find out these underlying factors. The analysis of hidden factors is performed in a binary way, i.e, the user only tells if they liked (rating 1) a specific movie or not (rating 0) and it represents the inputs for the input/visible layer.
C10999	Class boundaries are the data values which separate classes. They are not part of the classes or the dataset. The lower class boundary of a class is defined as the average of the lower limit of the class in question and the upper limit of the previous class.

C10900

How to Use K-means Cluster Algorithms in Predictive AnalysisPick k random items from the dataset and label them as cluster representatives.Associate each remaining item in the dataset with the nearest cluster representative, using a Euclidean distance calculated by a similarity function.Recalculate the new clusters' representatives.More items

C10901

Events are dependent if the outcome of one event affects the outcome of another. For example, if you draw two colored balls from a bag and the first ball is not replaced before you draw the second ball then the outcome of the second draw will be affected by the outcome of the first draw.

C10902

Systematic sampling is a type of probability sampling method in which sample members from a larger population are selected according to a random starting point but with a fixed, periodic interval. This interval, called the sampling interval, is calculated by dividing the population size by the desired sample size.

C10903

So unlike biological neurons, artificial neurons don't just “fire”: they send continuous values instead of binary signals. Depending on their activation functions, they might somewhat fire all the time, but the strength of these signals varies.

C10904

Any point (x) from a normal distribution can be converted to the standard normal distribution (z) with the formula z = (x-mean) / standard deviation. z for any particular x value shows how many standard deviations x is away from the mean for all x values.

C10905

To overcome the issue of the curse of dimensionality, Dimensionality Reduction is used to reduce the feature space with consideration by a set of principal features.

C10906

cortex

C10907

Feature extraction is a process of dimensionality reduction by which an initial set of raw data is reduced to more manageable groups for processing. A characteristic of these large data sets is a large number of variables that require a lot of computing resources to process.

C10908

Descriptive Analytics tells you what happened in the past. Predictive Analytics predicts what is most likely to happen in the future. Prescriptive Analytics recommends actions you can take to affect those outcomes.

C10909

KMeans is a clustering algorithm which divides observations into k clusters. Since we can dictate the amount of clusters, it can be easily used in classification where we divide data into clusters which can be equal to or more than the number of classes.

C10910

The relationship between margin of error and sample size is simple: As the sample size increases, the margin of error decreases. If you think about it, it makes sense that the more information you have, the more accurate your results are going to be (in other words, the smaller your margin of error will get).

C10911

In particular, a random experiment is a process by which we observe something uncertain. After the experiment, the result of the random experiment is known. An outcome is a result of a random experiment. The set of all possible outcomes is called the sample space.

C10912

Dummy variables are useful because they enable us to use a single regression equation to represent multiple groups. This means that we don't need to write out separate equation models for each subgroup. The dummy variables act like 'switches' that turn various parameters on and off in an equation.

C10913

How to Calculate a Confusion MatrixYou need a test dataset or a validation dataset with expected outcome values.Make a prediction for each row in your test dataset.From the expected outcomes and predictions count: The number of correct predictions for each class.

C10914

This implies that bias and variance of an estimator are complementary to each other i.e. an estimator with high bias will vary less(have low variance) and an estimator with high variance will have less bias(as it can vary more to fit/explain/estimate the data points).

C10915

Chebyshev's inequality, also known as Chebyshev's theorem, is a statistical tool that measures dispersion in a data population. The theorem states that no more than 1 / k2 of the distribution's values will be more than k standard deviations away from the mean.

C10916

Optimization is the most essential ingredient in the recipe of machine learning algorithms. It starts with defining some kind of loss function/cost function and ends with minimizing the it using one or the other optimization routine.

C10917

We can use the median with the interquartile range, or we can use the mean with the standard deviation.

C10918

Why gradient clipping accelerates training: A theoretical justification for adaptivity. These observations motivate us to introduce a novel relaxation of gradient smoothness that is weaker than the commonly used Lipschitz smoothness assumption.

C10919

As the formula shows, the standard score is simply the score, minus the mean score, divided by the standard deviation. Therefore, let's return to our two questions.

C10920

Learning of probability helps you in making informed decisions about likelihood of events, based on a pattern of collected data. In the context of data science, statistical inferences are often used to analyze or predict trends from data, and these inferences use probability distributions of data.

C10921

The decision tree splits the nodes on all available variables and then selects the split which results in most homogeneous sub-nodes. The ID3 algorithm builds decision trees using a top-down greedy search approach through the space of possible branches with no backtracking.

C10922

Sigmoid function, unlike step function, introduces non-linearity into our neural network model. This non-linear activation function, when used by each neuron in a multi-layer neural network, produces a new “representation” of the original data, and ultimately allows for non-linear decision boundary, such as XOR.

C10923

Examples of multivariate regression Example 2. A doctor has collected data on cholesterol, blood pressure, and weight. She also collected data on the eating habits of the subjects (e.g., how many ounces of red meat, fish, dairy products, and chocolate consumed per week).

C10924

The false discovery rate (FDR) is a statistical approach used in multiple hypothesis testing to correct for multiple comparisons. The FDR is defined as the expected proportion of false discoveries, i.e., incorrectly rejected null hypothesis, among all discoveries (Benjamini and Hochberg 1995).

C10925

A simple random sample is similar to a random sample. The difference between the two is that with a simple random sample, each object in the population has an equal chance of being chosen. With random sampling, each object does not necessarily have an equal chance of being chosen.

C10926

The other major way to estimate inter-rater reliability is appropriate when the measure is a continuous one. There, all you need to do is calculate the correlation between the ratings of the two observers. For instance, they might be rating the overall level of activity in a classroom on a 1-to-7 scale.

C10927

With cluster sampling, the researcher divides the population into separate groups, called clusters. Then, a simple random sample of clusters is selected from the population. For example, given equal sample sizes, cluster sampling usually provides less precision than either simple random sampling or stratified sampling.

C10928

A high pass filter can be formed by placing a capacitor in series with an inverting gain stage as shown in Figure 11.13.

C10929

Stochastic Gradient Descent: you would randomly select one of those training samples at each iteration to update your coefficients. Online Gradient Descent: you would use the "most recent" sample at each iteration. There is no stochasticity as you deterministically select your sample.

C10930

The clarity of visual features are excellent inputs to Deep Learning models. Because images can learn from themselves in a semi-supervised manner, there is no data required.

C10931

In short, fourier series is for periodic signals and fourier transform is for aperiodic signals. Fourier series is used to decompose signals into basis elements (complex exponentials) while fourier transforms are used to analyze signal in another domain (e.g. from time to frequency, or vice versa).

C10932

In statistics, the residual sum of squares (RSS), also known as the sum of squared residuals (SSR) or the sum of squared estimate of errors (SSE), is the sum of the squares of residuals (deviations predicted from actual empirical values of data).

C10933

Whereas the null hypothesis of the two-sample t test is equal means, the null hypothesis of the Wilcoxon test is usually taken as equal medians. Another way to think of the null is that the two populations have the same distribution with the same median.

C10934

To find the critical value, follow these steps.Compute alpha (α): α = 1 - (confidence level / 100)Find the critical probability (p*): p* = 1 - α/2.To express the critical value as a z-score, find the z-score having a cumulative probability equal to the critical probability (p*).More items

C10935

Statistically Valid Sample Size Criteria Probability or percentage: The percentage of people you expect to respond to your survey or campaign. Confidence: How confident you need to be that your data is accurate. Expressed as a percentage, the typical value is 95% or 0.95.

C10936

Click on the triangle-shaped icon located at the top right corner of the panel, and then choose "Save Path". Next, select "Clipping Path" from the same drop-down menu. A new dialog box will appear with a variety of clipping path settings. Make sure your path is selected, and then click OK.

C10937

Related calculationsFalse positive rate (α) = type I error = 1 − specificity = FP / (FP + TN) = 180 / (180 + 1820) = 9%False negative rate (β) = type II error = 1 − sensitivity = FN / (TP + FN) = 10 / (20 + 10) = 33%Power = sensitivity = 1 − βLisää kohteita…

C10938

To measure test-retest reliability, you conduct the same test on the same group of people at two different points in time. Then you calculate the correlation between the two sets of results.

C10939

In probability theory and statistics, the beta distribution is a family of continuous probability distributions defined on the interval [0, 1] parameterized by two positive shape parameters, denoted by α and β, that appear as exponents of the random variable and control the shape of the distribution.

C10940

An association rule has two parts: an antecedent (if) and a consequent (then). An antecedent is an item found within the data. Support is an indication of how frequently the items appear in the data. Confidence indicates the number of times the if-then statements are found true.

C10941

Inductive Learning is where we are given examples of a function in the form of data (x) and the output of the function (f(x)). The goal of inductive learning is to learn the function for new data (x). Classification: when the function being learned is discrete. Regression: when the function being learned is continuous.

C10942

There are four main types of probability sample.Simple random sampling. In a simple random sample, every member of the population has an equal chance of being selected. Systematic sampling. Stratified sampling. Cluster sampling.

C10943

receiver operating characteristic curve

C10944

The easiest[A] way to evaluate the actual value of a Tensor object is to pass it to the Session. run() method, or call Tensor. eval() when you have a default session (i.e. in a with tf. Session(): block, or see below).

C10945

To find the mean absolute deviation of the data, start by finding the mean of the data set. Find the sum of the data values, and divide the sum by the number of data values. Find the absolute value of the difference between each data value and the mean: |data value – mean|.

C10946

To address this issue, there is a modification to Cohen's kappa called weighted Cohen's kappa. The weighted kappa is calculated using a predefined table of weights which measure the degree of disagreement between the two raters, the higher the disagreement the higher the weight.

C10947

NAT (Network Address Translation) is a feature of the Firewall Software Blade and replaces IPv4 and IPv6 addresses to add more security. You can enable NAT for all SmartDashboard objects to help manage network traffic. NAT protects the identity of a network and does not show internal IP addresses to the Internet.

C10948

An F-test (Snedecor and Cochran, 1983) is used to test if the variances of two populations are equal. This test can be a two-tailed test or a one-tailed test. The two-tailed version tests against the alternative that the variances are not equal.

C10949

Multinomial logistic regression (often just called 'multinomial regression') is used to predict a nominal dependent variable given one or more independent variables. It is sometimes considered an extension of binomial logistic regression to allow for a dependent variable with more than two categories.

C10950

Nonparametric statistics refers to a statistical method in which the data are not assumed to come from prescribed models that are determined by a small number of parameters; examples of such models include the normal distribution model and the linear regression model.

C10951

What Are Moments in Statistics?Moments About the MeanFirst, calculate the mean of the values.Next, subtract this mean from each value.Then raise each of these differences to the sth power.Now add the numbers from step #3 together.Finally, divide this sum by the number of values we started with.

C10952

Random assignment is however a process of randomly assigning subjects to experimental or control groups. This is a standard practice in true experimental research to ensure that treatment groups are similar (equivalent) to each other and to the control group, prior to treatment administration.

C10953

A quantile defines a particular part of a data set, i.e. a quantile determines how many values in a distribution are above or below a certain limit. Special quantiles are the quartile (quarter), the quintile (fifth) and percentiles (hundredth).

C10954

A classification is an ordered set of related categories used to group data according to its similarities. It consists of codes and descriptors and allows survey responses to be put into meaningful categories in order to produce useful data. A classification is a useful tool for anyone developing statistical surveys.

C10955

6 Practices to enhance the performance of a Text ClassificationDomain Specific Features in the Corpus. For a classification problem, it is important to choose the test and training corpus very carefully. Use An Exhaustive Stopword List. Noise Free Corpus. Eliminating features with extremely low frequency. Normalized Corpus. Use Complex Features: n-grams and part of speech tags.

C10956

An eager algorithm executes immediately and returns a result. A lazy algorithm defers computation until it is necessary to execute and then produces a result. Eager algorithms are easier to understand and debug. They can also be highly optimized for a single use case (e.g. filter ).

C10957

In representation learning, features are extracted from unlabeled data by training a neural network on a secondary, supervised learning task. When applying deep learning to natural language processing (NLP) tasks, the model must simultaneously learn several language concepts: the meanings of words.

C10958

Data augmentation in data analysis are techniques used to increase the amount of data by adding slightly modified copies of already existing data or newly created synthetic data from existing data. It acts as a regularizer and helps reduce overfitting when training a machine learning model.

C10959

Artificial intelligence has close connections with philosophy because both use concepts that have the same names and these include intelligence, action, consciousness, epistemology, and even free will. These factors contributed to the emergence of the philosophy of artificial intelligence.

C10960

Conclusion. Cross-Validation is a very powerful tool. It helps us better use our data, and it gives us much more information about our algorithm performance. In complex machine learning models, it's sometimes easy not pay enough attention and use the same data in different steps of the pipeline.

C10961

Clustering is done based on a similarity measure to group similar data objects together. This similarity measure is most commonly and in most applications based on distance functions such as Euclidean distance, Manhattan distance, Minkowski distance, Cosine similarity, etc. to group objects in clusters.

C10962

A composite hypothesis test contains more than one parameter and more than one model. In a simple hypothesis test, the probability density functions for both the null hypothesis (H0) and alternate hypothesis (H1) are known.

C10963

Receptive field, region in the sensory periphery within which stimuli can influence the electrical activity of sensory cells.

C10964

The cumulative density function gives you the probability of a random variable being on or below a certain value. The quantile function is the opposite of that. i.e. you give it a probability and it tells you the random variable value. A quartile is the value of the quantile at the probabilities 0.25, 0.5 and 0.75.

C10965

In general, if the data is normally distributed, parametric tests should be used. If the data is non-normal, non-parametric tests should be used.

C10966

So when you perform t-test for comparison of two means or ANOVA forr comparison of multiple means. You need dummy variables. In your case if the data is categorical you'll definitely need to convert them so simultaneously they are becoming dummy by themselves. Hence YES, you can use these tests for categorical data.

C10967

Server clustering refers to a group of servers working together on one system to provide users with higher availability. These clusters are used to reduce downtime and outages by allowing another server to take over in the event of an outage. Here's how it works. A group of servers are connected to a single system.

C10968

The shape of any distribution can be described by its various 'moments'. The first four are: 1) The mean, which indicates the central tendency of a distribution. 2) The second moment is the variance, which indicates the width or deviation.

C10969

A common cause of sampling bias lies in the design of the study or in the data collection procedure, both of which may favor or disfavor collecting data from certain classes or individuals or in certain conditions. Figure 1: Possible sources of bias occurring in the selection of a sample from a population.

C10970

12:3117:15Suggested clip · 120 secondsLogistic Regression in R, Clearly Explained!!!! - YouTubeYouTubeStart of suggested clipEnd of suggested clip

C10971

Dependent events: Two events are dependent when the outcome of the first event influences the outcome of the second event. The probability of two dependent events is the product of the probability of X and the probability of Y AFTER X occurs.

C10972

A bounding box is an imaginary rectangle that serves as a point of reference for object detection and creates a collision box for that object. Data annotators draw these rectangles over images, outlining the object of interest within each image by defining its X and Y coordinates.

C10973

The Least Squares Regression Line is the line that makes the vertical distance from the data points to the regression line as small as possible. It's called a “least squares” because the best line of fit is one that minimizes the variance (the sum of squares of the errors).

C10974

Bagging is a way to decrease the variance in the prediction by generating additional data for training from dataset using combinations with repetitions to produce multi-sets of the original data. Boosting is an iterative technique which adjusts the weight of an observation based on the last classification.

C10975

Response bias is a general term for a wide range of tendencies for participants to respond inaccurately or falsely to questions. These biases are prevalent in research involving participant self-report, such as structured interviews or surveys.

C10976

The standard deviation formula may look confusing, but it will make sense after we break it down. Step 1: Find the mean.Step 2: For each data point, find the square of its distance to the mean.Step 3: Sum the values from Step 2.Step 4: Divide by the number of data points.Step 5: Take the square root.

C10977

“Human error” is not a source of experimental error. You must classify specific errors as random or systematic and identify the source of the error. Human error cannot be stated as experimental error.

C10978

Binning is a way to group a number of more or less continuous values into a smaller number of "bins". For example, if you have data about a group of people, you might want to arrange their ages into a smaller number of age intervals.

C10979

Interpret the key results for Binary Logistic RegressionStep 1: Determine whether the association between the response and the term is statistically significant.Step 2: Understand the effects of the predictors.Step 3: Determine how well the model fits your data.Step 4: Determine whether the model does not fit the data.

C10980

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).

C10981

Bias in data can result from: survey questions that are constructed with a particular slant. choosing a known group with a particular background to respond to surveys. reporting data in misleading categorical groupings.

C10982

Tensorflow is the most famous library used in production for deep learning models. However TensorFlow is not that easy to use. On the other hand, Keras is a high level API built on TensorFlow (and can be used on top of Theano too). It is more user-friendly and easy to use as compared to TF.

C10983

If you are working on a classification problem, the best score is 100% accuracy. If you are working on a regression problem, the best score is 0.0 error. These scores are an impossible to achieve upper/lower bound.

C10984

Converting a Covariance Matrix to a Correlation Matrix First, use the DIAG function to extract the variances from the diagonal elements of the covariance matrix. Then invert the matrix to form the diagonal matrix with diagonal elements that are the reciprocals of the standard deviations.

C10985

In the context of neural networks, a perceptron is an artificial neuron using the Heaviside step function as the activation function. The perceptron algorithm is also termed the single-layer perceptron, to distinguish it from a multilayer perceptron, which is a misnomer for a more complicated neural network.

C10986

In the field of artificial intelligence, inference engine is a component of the system that applies logical rules to the knowledge base to deduce new information. The first inference engines were components of expert systems. The knowledge base stored facts about the world.

C10987

In fact, linear regression analysis works well, even with non-normal errors.

C10988

Let's take a look at some of the important business problems solved by machine learning.Manual data entry. Detecting Spam. Product recommendation. Medical Diagnosis. Customer segmentation and Lifetime value prediction. Financial analysis. Predictive maintenance. Image recognition (Computer Vision)

C10989

R is now used by over 50% of data miners. R, Python, and SQL were the most popular programming languages. Python, Lisp/Clojure, and Unix tools showest the highest growth in 2012, while Java and MATLAB slightly declined in popularity.

C10990

The four elements of a descriptive statistics problem include population/sample, tables/graphs, identifying patterns, and A. data.

C10991

The 7 Steps of Machine Learning1 - Data Collection. The quantity & quality of your data dictate how accurate our model is. 2 - Data Preparation. Wrangle data and prepare it for training. 3 - Choose a Model. 4 - Train the Model. 5 - Evaluate the Model. 6 - Parameter Tuning. 7 - Make Predictions.

C10992

The neuron is the basic working unit of the brain, a specialized cell designed to transmit information to other nerve cells, muscle, or gland cells. Neurons are cells within the nervous system that transmit information to other nerve cells, muscle, or gland cells. Most neurons have a cell body, an axon, and dendrites.

C10993

Taking the square root of the variance gives us the units used in the original scale and this is the standard deviation. Standard deviation is the measure of spread most commonly used in statistical practice when the mean is used to calculate central tendency. Thus, it measures spread around the mean.

C10994

Many time series show periodic behavior. This periodic behavior can be very complex. Spectral analysis is a technique that allows us to discover underlying periodicities. To perform spectral analysis, we first must transform data from time domain to frequency domain.

C10995

0:294:16Suggested clip · 116 secondsGeometric distribution moment generating function - YouTubeYouTubeStart of suggested clipEnd of suggested clip

C10996

In a multilevel model, we use random variables to model the variation between groups. An alternative approach is to use an ordinary regression model, but to include a set of dummy variables to represent the differences between the groups. The multilevel approach offers several advantages.

C10997

Quantum fields are matter. The simplest “practical” quantum field theory is quantum electromagnetism. In it, two fields exist: the electromagnetic field and the “electron field”. These two fields continuously interact with each other, energy and momentum are transferred, and excitations are created or destroyed.

C10998

Restricted Boltzmann Machines are used to analyze and find out these underlying factors. The analysis of hidden factors is performed in a binary way, i.e, the user only tells if they liked (rating 1) a specific movie or not (rating 0) and it represents the inputs for the input/visible layer.

C10999

Class boundaries are the data values which separate classes. They are not part of the classes or the dataset. The lower class boundary of a class is defined as the average of the lower limit of the class in question and the upper limit of the previous class.