Data Mining is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. It is an interdisciplinary subfield of computer science. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further … Read more
Cross-Validation is a model validation technique for assessing how the results of a statistical analysis will generalize to an independent data set. It is mainly used in settings where the goal is a prediction, and one wants to estimate how accurately a predictive model will perform in practice. The idea is to define a dataset to “test” the model in … Read more
Correlation is a statistical measure that can show whether and how strongly pairs of variables are related. For example, height and weight are related; taller people tend to be heavier than shorter people. The relationship isn’t perfect. People of the same height vary in weight, and you can easily think of two people you know where the shorter one is … Read more
Convolutional Neural Network (CNN) is made up of neurons that have learnable weights and biases. CNN are a category of Neural Networks that have proven very effective in areas such as image recognition and classification. ConvNets have been successful in identifying faces, objects and traffic signs apart from powering vision in robots and self-driving cars. CNNs transform the original image … Read more
Collaborative Filtering (CF) is a technique used by recommender systems. Collaborative filtering has two senses, a narrow one, and a more general one. Collaborative filtering is a method of making automatic predictions (filtering) about the interests of a user by collecting preferences or taste information from many users (collaborating). The underlying assumption of the collaborative filtering approach is that if … Read more
Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in one sense or another) to each other than to those in other groups (clusters). It is the main task of exploratory data mining, and a common technique for statistical data … Read more
Classification in machine learning and statistics, is the problem of identifying to which of a set of categories (sub-populations) a new observation belongs, on the basis of a training set of data containing observations (or instances) whose category membership is known. Classification is an example of pattern recognition. In the terminology of machine learning, classification is considered an instance of … Read more
Chi-squared test for variances. A chi-square test can be used to test if the variance of a population is equal to a specified value. This test can be either a two-sided test or a one-sided test. The two-sided version tests against the alternative that the true variance is either less than or greater than the specified value. The one-sided version … Read more
Chi-squared test for goodness of fit also written as a χ2 test is any statistical hypothesis test wherein the sampling distribution of the test statistic is a chi-squared distribution when the null hypothesis is true. Without other qualification, ‘chi-squared test’ often is used as short for Pearson’s chi-squared test. Chi-squared tests are often constructed from a sum of squared errors, … Read more
Central Limit Theorem (CLT) is a statistical theory that states that given a sufficiently large sample size from a population with a finite level of variance, the mean of all samples from the same population will be approximately equal to the mean of the population. Furthermore, all of the samples will follow an approximately normal distribution pattern, with all variances … Read more