For binary classification problems, a good way to measure the performance of a model is by finding out AUC (Area Under The Curve) of ROC (Receiver Operating Characteristics). What is a ROC curve? It is a plot of True Positive Rate(TPR) vs FPR at various thresholding levels. Let’s understand this Read more…
Confusion Matrix Confusion Matrix is a group or matrix of metrics in supervised learning scenarios which determine how good a model is in predictions. Lets consider a binary classifier with results 1 or 0. A confusion matrix here will be a 2×2 matrix where along one axis you have actual Read more…
Hypothesis Testing Hypothesis tests, also called significance tests, are ubiquitous in the traditional statistical analysis of published research. Their purpose is to help you learn whether random chance might be responsible for an observed effect. KEY TERMS Null hypothesis The hypothesis that chance is to blame. Alternative hypothesis The counterpoint Read more…
This is part 2 of Introduction to Dimensionality Reduction. In this blog post, we would several different mathematical prerequisites that one must know before trying to understand machine learning. Mean Vector The sample mean is a vector each of whose elements is the sample mean of one of the random Read more…
Introduction to Dimensionality Reduction There are several ways we can define Dimensionality Reduction. One way to define it is: Dimensionality Reduction refers to the process of converting a set of data having vast dimensions into data with lesser dimensions ensuring that it conveys similar information concisely. The other way which Read more…
Before applying any dimensionality reduction technique sometimes it is important to preprocess the data. There are several ways which we can use for preprocessing data. In this post, we will explore one of the common ways to do data preprocessing which is column normalization (or feature scaling). Feature scaling Feature Read more…
In statistics, a confidence interval (CI) is a type of interval estimate which we compute using the statistics of the observed data. The interval has an associated confidence level that, loosely speaking, quantifies the level of confidence that the value of the parameter lies in the interval. For eg, if Read more…
Suppose you have taken the data for heights and weights of students in class and you want to figure out the correlation between heights and weights of students. The relation between these two parameters is defined mathematically by one of the 3 ways 1) Covariance 2) Pearson Correlation Coefficient 3) Read more…
Types of Distributions Bernoulli and Binomial distribution A Bernoulli random variable has two possible outcomes: 0 or 1. A binomial distribution is the sum of independent and identically distributed Bernoulli random variables. So, for example, say I have a coin, and, when tossed, the probability it lands heads is p. Read more…
Populations and Samples The main difference between a population and a sample has to do with how observations are assigned to the data set Population Includes all of the elements from a set of data. Sample Consists of one or more observations from the population. Probability Density function(PDF) The PDF, Read more…