## What is Unsupervised Learning?

Unsupervised Learning is a type of machine learning algorithm used to draw inferences from datasets consisting of input data without labelled responses. The most common unsupervised learning method is cluster analysis, which is used for exploratory data analysis to find hidden patterns or grouping in data. The clusters are modelled…

## What is Type II Error?

Type II Error in statistical hypothesis testing is incorrectly retaining a false null hypothesis (a “false negative”). A type II error (or error of the second kind) is the failure to reject a false null hypothesis. Examples of type II errors would be a blood test failing to detect the…

## What is Type I Error?

Type I Error in statistical hypothesis testing is the incorrect rejection of a true null hypothesis (a false positive). More simply stated, a type I error is detecting an effect that is not present. A type I error (or error of the first kind) is the incorrect rejection of a…

## What is True Positive Rate (Sensitivity)?

True Positive Rate (Sensitivity) is a statistical measure which measures the proportion of positives that are correctly identified as such (for example, the percentage of sick people who are correctly identified as having the condition). Another way to understand it, with examples in the context of medical tests is that…

## What is True Negative Rate (Specificity)?

True Negative Rate (Specificity) is a statistical measure which measures the proportion of negatives that are correctly identified as such (for example, the percentage of healthy people who are correctly identified as not having the condition). Specificity is the extent to which positives really represent the condition of interest and…

## What is Three Sigma Rule?

Three Sigma Rule in the empirical sciences express a conventional heuristic that “nearly all” values are taken to lie within three standard deviations of the mean, i.e. that it is empirically useful to treat 99.7% probability as “near certainty”.The rule states that even for non-normally distributed variables, at least 88.8%…

## What is Support Vector Machines (SVM)?

Support Vector Machines (SVM) is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data (supervised learning), the algorithm outputs an optimal hyperplane which categorizes new examples. More formally, a support vector machine constructs a hyperplane or set of hyperplanes in a high- or…

## What is Supervised Learning?

Supervised Learning is the machine learning task of inferring a function from labeled training data. The training data consist of a set of training examples. In supervised learning, each example is a pair consisting of an input object (typically a vector) and the desired output value (also called the supervisory…

## What is Statistical Significance?

Statistical Significance in statistical hypothesis testing is attained whenever the observed p-value of a test statistic is less than the significance level defined for the study. The p-value is the probability of obtaining results at least as extreme as those observed, given that the null hypothesis is true. The significance…

## What is Statistical Power?

Statistical Power of any test of statistical significance is defined as the probability that it will reject a false null hypothesis. Statistical power is inversely related to beta or the probability of making a Type II error. The power is a function of the possible distributions, often determined by a…

## What is Sentiment Analysis?

Sentiment Analysis refers to the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information. Sentiment analysis is widely applied to voice of the customer materials such as reviews and survey responses, online and social media, and…

## What is Semi-Supervised Learning?

Semi-Supervised Learning is a class of supervised learning tasks that also make use of unlabeled data for training – typically a small amount of labeled data with a large amount of unlabelled data. Semi-supervised learning falls between unsupervised learning (without any labeled training data) and supervised learning (with completely labeled…

## What is Semantic Indexing or Latent Semantic Indexing (LSI)?

Semantic Indexing or Latent Semantic Indexing (LSI) is a mathematical method used to determine the relationship between terms and concepts in content. The contents of a web page are crawled by a search engine and the most common words and phrases are collated and identified as the keywords for the…

## What is Self-Organizing Map (SOM)?

Self-Organizing Map (SOM) is a type of artificial neural network that is trained using unsupervised learning to produce a low-dimensional (typically two-dimensional), discretized representation of the input space of the training samples, called a map, and is, therefore, a method to do dimensionality reduction. Self-organizing maps differ from other artificial…

## What is Selection Bias?

Selection Bias is the selection of individuals, groups or data for analysis in such a way that proper randomization is not achieved, thereby ensuring that the sample obtained is not representative of the population intended to be analyzed. It is sometimes referred to as the selection effect. The phrase “selection…

## What is R-squared?

R-squared is a statistical measure of how close the data are to the fitted regression line. It is also known as the coefficient of determination, or the coefficient of multiple determination for multiple regression. R-squared is the percentage of the response variable variation that is explained by the model, it…

## What is Root Mean Square Error (RMSE)?

Root Mean Square Error (RMSE) is a frequently used measure of the differences between values (sample and population values) predicted by a model or an estimator and the values actually observed. The RMSD represents the sample standard deviation of the differences between predicted values and observed values. These individual differences…

## What is Resampling?

Resampling is any technique of generating a new sample from an existing dataset. There is a variety of methods for estimating the precision of sample statistics (medians, variances, percentiles) by using subsets of available data (jackknifing) or drawing randomly with replacement from a set of data points (bootstrapping). Exchanging labels…

## What is Regularization?

Regularization in the field of machine learning is a process of introducing additional information in order to solve an ill-posed problem or to prevent overfitting. A theoretical justification for regularization is that it attempts to impose Occam’s razor on the solution, as depicted in the figure. From a Bayesian point…

## What is Regression?

Regression is a statistical measure used that attempts to determine the strength of the relationship between one dependent variable and a series of other changing (independent) variables. The two basic types of regression are linear regression and multiple linear regression, although there are non-linear regression methods for more complicated data…

## What is Random Sampling?

Random sampling. In this technique, each member of the population has an equal chance of being selected as the subject. The entire process of sampling is done in a single step with each subject selected independently of the other members of the population. There are many methods to proceed with…

## What is Random Forest?

Random Forest or Random Decision Forest are an ensemble learning method for classification, regression, and other tasks, that operate by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees. Random…

## What is Radial Basis Function(RBF) network?

Radial Basis Function(RBF) network is an artificial neural network that uses radial basis functions as activation functions. The output of the network is a linear combination of radial basis functions of the inputs and neuron parameters. Radial basis function networks have many uses, including function approximation, time series prediction, classification,…

## What is QQ plot?

QQ plots – Quantile-Quantile plots are a graphical technique for determining if two data sets come from populations with a common distribution. A q-q plot is a plot of the quantiles of the first data set against the quantiles of the second data set. By a quantile, we mean the…

## What is Q-learning?

Q-learning is a model-free reinforcement learning technique. Specifically, Q-learning can be used to find an optimal action selection policy for any given (finite) Markov decision process (MDP). It works by learning an action-value function that ultimately gives the expected utility of taking a given action in a given state and…

## What is Pruning?

Pruning is a technique in machine learning that reduces the size of decision trees by removing sections of the tree that provide a little power to classify instances. Pruning reduces the complexity of the final classifier, and hence improves predictive accuracy by the reduction of overfitting. One of the questions…

## What is Probabilistic Neural Network (PNN)?

Probabilistic Neural Network (PNN) is kind of feedforward neural network. In the PNN algorithm, the parent probability distribution function (PDF) of each class is approximated by a Parzen window and a non-parametric function. Then, using PDF of each class, the class probability of a new input data is estimated and…

## What is Principal Component Analysis (PCA) ?

Principal Component Analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components (or sometimes, principal modes of variation). The number of principal components is less than or…

## What is Predictive Modeling?

Predictive Modeling is a process through which a future outcome or behavior is predicted based on the past and current data at hand. It is a statistical analysis technique that enables the evaluation and calculation of the probability of certain results. Predictive modeling works by collecting data, creating a statistical…

## What is Power Analysis?

Power Analysis is an important aspect of experimental design. It allows us to determine the sample size required to detect an effect of a given size with a given degree of confidence. There are four parameters involved in a power analysis. The research must ‘know’ 3 and solve for the 4th….

## What is Paired t-Test?

Paired t-Test has its purpose in the testing is to determine whether there is statistical evidence that the mean difference between paired observations on a particular outcome is significantly different from zero. The Paired-Samples t Test is a parametric test. This test is also known as Dependent t-Test. Was the…

## What is Overfitting?

Overfitting in mathematics and statistics is one of the most common tasks consisting in attempts to fit a “model” to a set of training data, so as to be able to make reliable predictions on generally untrained data. In overfitting, a statistical model describes random error or noise instead of…

## What is Out-Of-Sample Evaluation?

Out-Of-Sample Evaluation means to withhold some of the sample data from the model identification and estimation process, then use the model to make predictions for the hold-out data in order to see how accurate they are and to determine whether the statistics of their errors are similar to those that…

## What is Outlier?

Outlier is an observation point that is distant from other observations. An outlier may be due to variability in the measurement or it may indicate an experimental error, the latter are sometimes excluded from the data set. Outliers can occur by chance in any distribution, but they often indicate either…

## What is Nearest Neighbor Algorithm?

Nearest Neighbor Algorithm was one of the first algorithms used to determine a solution to the traveling salesman problem. In it, the salesman starts in a random city and repeatedly visits the nearest city until all have been visited. It quickly yields a short tour, but usually not the optimal…

## What is Multiple Regression?

Multiple Regression is an extension of simple linear regression. It is used when we want to predict the value of a variable based on the value of two or more other variables. The variable we want to predict is called the dependent variable (or sometimes, the outcome, target or criterion…

## What is Multinomial Logistic Regression?

Multinomial Logistic Regression is the linear regression analysis to conduct when the dependent variable is nominal with more than two levels. Thus it is an extension of logistic regression, which analyzes dichotomous (binary) dependents. Since the output of the analysis is somewhat different to the logistic regression’s output, multinomial regression…

## What is Model Fitting ?

Model Fitting is running an algorithm to learn the relationship between predictors and outcome so that you can predict the future values of the outcome. It proceeds in three steps: First, you need a function that takes in a set of parameters and returns a predicted data set. Second you…

## What is Markov Model?

Markov Model in probability theory is a stochastic model used to model randomly changing systems where it is assumed that future states depend only on the current state not on the events that occurred before it (defined as the Markov property). Generally, this assumption enables reasoning and computation with the…

## What is Manhattan Distance?

Manhattan Distance is the distance between two points measured along axes at right angles. The name hints to the grid layout of the streets of Manhattan, which causes the shortest path a car could take between two points in the city. The limitation of the Manhattan Distance heuristic is that…

## What is MAE (Mean Absolute Error)?

MAE – Mean Absolute Error in statistics is a quantity used to measure how close forecasts or predictions are to the eventual outcomes.The mean absolute error is an average of the absolute error where is the prediction and the true value. Note that alternative formulations may include relative frequencies as…

## What is Machine Translation (MT)?

Machine Translation (MT) is a sub-field of computational linguistics that investigates the use of software to translate text or speech from one language to another. On a basic level, MT performs simple substitution of words in one language for words in another, but that alone usually cannot produce a good…

## What is Loss Function?

Loss Function in mathematical optimization, statistics, decision theory and machine learning is a function that maps an event or values of one or more variables onto a real number intuitively representing some “cost” associated with the event. An optimization problem seeks to minimize a loss function. An objective function is…

## What is LOOCV or Leave-One-Out Cross Validation?

LOOCV or Leave-One-Out Cross Validation. LOOCV uses one observation from the original sample as the validation data, and the remaining observations as the training data. This is repeated such that each observation in the sample is used once as the validation data. This is the same as a K-fold cross-validation…

## What is Long-Tailed Distribution?

Long-Tailed Distribution in statistics and business is the portion of the distribution having a large number of occurrences far from the “head” or central part of the distribution. The term is often used loosely, with no definition or arbitrary definition, but precise definitions are possible. Broadly speaking, for such population…

## What is Long Short-Term Memory(LSTM) in machine learning?

Long Short-Term Memory usually just called “LSTMs” – are a special kind of RNN, capable of learning long-term dependencies. LSTMs are explicitly designed to avoid the long-term dependency problem. Remembering information for long periods of time is their default behavior. All recurrent neural networks have the form of a chain…

## What is Log-Normal Distribution?

Log-Normal Distribution in probability theory is a continuous probability distribution of a random variable whose logarithm is normally distributed. Thus, if the random variable is log-normally distributed, then has a normal distribution. Likewise, if Y has a normal distribution, then X=exp(y) has a log-normal distribution. A random variable which is…

## What is Logistic Regression?

Logistic Regression in statistics is a regression model where the dependent variable is categorical. For example the case of a binary dependent variable—that is, where it can take only two values, “0” and “1”, which represent outcomes such as pass/fail, win/lose, alive/dead or healthy/sick. Cases where the dependent variable has…

## What is Log Loss?

Log Loss measures the performance of a classification model where the prediction input is a probability value between 0 and 1. The goal of our machine learning models is to minimize this value. A perfect model would have a log loss of 0. Log loss increases as the predicted probability…

## What is Linear Regression ?

Linear Regression is an approach for modeling the relationship between a scalar dependent variable y and one or more explanatory variables (or independent variables) denoted X. The case of one explanatory variable is called simple linear regression. For more than one explanatory variable, the process is called multiple linear regression….

## What are Linear Classifiers ?

Linear Classifiers use object’s characteristics to predict which class (or group) it belongs to. It achieves this by making a classification decision based on the value of a linear combination of the characteristics. An object’s characteristics are also known as feature values and are typically presented to the machine in…

## What is Lazy Learning in machine learning?

Lazy Learning in machine learning is a learning method in which generalization beyond the training data is delayed until a query is made to the system, as opposed to in eager learning, where the system tries to generalize the training data before receiving queries. Lazy learning is essentially an instance-based…

## What is Law of Large Numbers ?

Law of Large Numbers is a principle of probability according to which the frequencies of events with the same likelihood of occurrence even out, given enough trials or instances. As the number of experiments increases, the actual ratio of outcomes will converge on the theoretical, or expected, a ratio of…

## What is Latent Semantic Indexing (LSI)?

Latent Semantic Indexing (LSI) is a mathematical method used to determine the relationship between terms and concepts in content. The contents of a web page are crawled by a search engine and the most common words and phrases are collated and identified as the keywords for the page. LSI looks…

## What is Lasso (Least Absolute Shrinkage And Selection Operator) ?

Lasso (Least Absolute Shrinkage And Selection Operator) in statistics and machine learning is a regression analysis method that performs both variable selection and regularization in order to enhance the prediction accuracy and interpretability of the statistical model it produces. Lasso was originally formulated for least squares models and this simple…

## What is Kolmogorov-Smirnov test?

Kolmogorov-Smirnov test (K–S test or KS test) is a nonparametric test of the equality of continuous, one-dimensional probability distributions that can be used to compare a sample with a reference probability distribution (one-sample K–S test), or to compare two samples (two-sample K–S test). The Kolmogorov–Smirnov statistic quantifies a distance between…

## What is K-Nearest Neighbour (KNN)?

K-Nearest Neighbour (KNN) in pattern recognition is a non-parametric method used for classification and regression. In both cases, the input consists of the k closest training examples in the feature space. The output depends on whether k-NN is used for classification or regression. In k-NN classification, the output is a…

## What is K-means Clustering?

K-means Clustering is a method of vector quantization, originally from signal processing, that is popular for cluster analysis in data mining. K-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster….

## What is K-means Algorithm in machine learning?

K-means Algorithm is one of the simplest unsupervised learning algorithms that solve the well-known clustering problem. The procedure follows a simple and easy way to classify a given data set through a certain number of clusters (assume k clusters) fixed a priori. The main idea is to define k centroids,…

## What is Kernel Trick ?

Kernel Trick is an approach consisting in the use of kernel functions, operating in a high-dimensional, implicit feature space without ever computing the coordinates of the data in that space, but rather by simply computing the inner products between the images of all pairs of data in the feature space….

## What is Jackknife Resampling (Jacknifing)?

Jackknife Resampling (Jacknifing) in statistics is a resampling technique especially useful for variance and bias estimation. The jackknife predates other common resampling methods such as the bootstrap. The jackknife estimator of a parameter is found by systematically leaving out each observation from a dataset and calculating the estimate and then…

## What is Interpolation?

Interpolation in the mathematical field of numerical analysis, is a method of constructing new data points within the range of a discrete set of known data points. In engineering and science, one often has a number of data points, obtained by sampling or experimentation, which represent the values of a…

## What is Intercept?

Intercept is the expected mean value of Y when all X=0. If we start with a regression equation with one predictor, X. If X sometimes is equal zero, the intercept is simply the expected mean value of Y at that value. If X never equals zero, then the intercept has…

## What is Information Retrieval (IR) ?

Information Retrieval (IR) is the action/process of obtaining information resources relevant to an information need from a collection of information resources. Searches can be based on full-text or other content-based indexing. Information retrieval is the science of searching for information in a document, searching for documents themselves, searching for metadata…

## What is Hopfield Network?

Hopfield Network is a form of recurrent artificial neural network. Hopfield networks are classical models of memory and collective processing in networks of abstract McCulloch-Pitts neurons, but they have not been widely used in signal processing as they usually have small memory capacity (scaling linearly in the number of neurons)…

## What is Homoscedastic?

Homoscedastic ‘in statistics, is a definition of a sequence or a vector of random variables if all random variables in the sequence or vector have the same finite variance. This is also known as the homogeneity of variance. The complementary notion is called heteroscedasticity’. Was the above useful? Please share…

## What is Hierarchical Clustering?

Hierarchical Clustering in data mining and statistics (also called hierarchical cluster analysis or HCA) is a method of cluster analysis which seeks to build a hierarchy of clusters. Strategies for hierarchical clustering generally fall into two types: Agglomerative: This is a “bottom up” approach: each observation starts in its own…

## What is Hidden Markov Model (HMM)?

Hidden Markov Model (HMM) is a statistical Markov model in which the system being modeled is assumed to be a Markov process with unobserved (hidden) states. An HMM can be presented as the simplest dynamic Bayesian network. In simpler Markov models (like a Markov chain), the state is directly visible…

## What is Heteroscedasticity?

Heteroscedasticity refers to the circumstance in which the variability of a variable is unequal across the range of values of a second variable that predicts it. In other words, it refers to data with unequal variability (scatter) across a set of second, predictor variables. Was the above useful? Please share…

## What is Hash Table (Hash Map)?

Hash Table (Hash Map) in computing is a data structure used to implement an associative array, a structure that can map keys to values. A hash table uses a hash function to compute an index into an array of buckets or slots, from which the desired value can be found….

## What is Gini Coefficient?

Gini Coefficient is a measure of statistical dispersion intended to represent the income or wealth distribution of a nation’s residents and is the most commonly used measure of inequality. The Gini coefficient measures the inequality among values of a frequency distribution (for example, levels of income). A Gini coefficient of…

## What is Gaussian Distribution (Normal Distribution)?

Gaussian Distribution (Normal Distribution) in probability theory is a very common continuous probability distribution. Normal distribution is important in statistics and is often used in the natural and social sciences to represent real-valued random variables whose distributions are not known. The normal distribution is useful because of the central limit…

## What is Fuzzy Clustering?

Fuzzy Clustering (also referred to as soft clustering) is a form of clustering in which each data point can belong to more than one cluster. Clustering or cluster analysis involves assigning data points to clusters (also called buckets, bins, or classes), or homogeneous classes, such that items in the same…

## What is Feedforward Neural Network (FNN)?

Feedforward Neural Network (FNN) is a biologically inspired classification algorithm. It consists of a (possibly large) number of simple neuron-like processing units, organized in layers. Every unit in a layer is connected with units in the previous layer. These connections are not all equal: each connection may have a different…

## What is Feature Vector?

Feature vector in pattern recognition and machine learning is an n-dimensional vector of numerical features that represent some object. Many algorithms in machine learning require a numerical representation of objects since such representations facilitate processing and statistical analysis. When representing images, the feature values might correspond to the pixels of…

## What is Feature in machine learning?

Feature in machine learning and pattern recognition is an individual measurable property of a phenomenon being observed. Choosing informative, discriminating and independent features is a crucial step for effective algorithms in pattern recognition, classification, and regression. Features are usually numeric, but structural features such as strings and graphs are used…

## What are False Negatives?

False negatives are where a test result indicates that a condition failed, while it was successful. I.e. erroneously no effect has been assumed. A common example is a guilty prisoner freed from jail. The condition: “Is the prisoner guilty?” is true (yes, the prisoner is guilty). But the test (a…

## What are False Positives?

False positives commonly called a “false alarm”, is a result that indicates a given condition has been fulfilled when it has not. I.e. erroneously a positive effect has been assumed. In the case of “crying wolf” – the condition tested for was “is there a wolf near the herd?”; the…

## What is Extrapolation?

Extrapolation in mathematics is the process of estimating, beyond the original observation range, the value of a variable on the basis of its relationship with another variable. It is similar to interpolation, which produces estimates between known observations, but extrapolation is subject to greater uncertainty and a higher risk of…

## What is Explanatory Data Analysis?

Explanatory Data Analysis (EDA) in statistics is an approach to analyzing data sets to summarize their main characteristics, often with visual methods. A statistical model can be used or not, but primarily EDA is for seeing what the data can tell us beyond the formal modeling or hypothesis testing task….