Variance and Standard Deviation are the most commonly used measures of variability and spread. Variability and spread are nothing but the process to know how much data is being varying from the mean point.
Principal Component Analysis or PCA is used for dimensionality reduction of the large data set. Using PCA we can speed-up the ML algorithms by reducing the feature spaces.
Logistic regression is used for binary classification problem which has only two classes to predict. However with little extension and some human brain, it can easily be used for multi class classification problem. In this post I will be explaining about binary classification. I will also explain about the reason behind maximizing log likelihood function.
Types of Statistics: Descriptive vs Inferential
Basic terminology like Population vs Sample
Types of Variables: Numerical vs Categorical
Measures of central tendencies: Mean, Median and Mode and their specific use cases
Measures of dispersion/spread: Variance, standard deviation etc.
The Coefficient of Determination is the measure of the variance in response variable ‘y’ that can be predicted using predictor variable ‘x’. It is the most common way to measure the strength of the model.
Linear Regression is a field of study which emphasizes on the statistical relationship between two continuous variables known as Predictor and Response variables. Predictor variable is most often denoted as x and also known as Independent variable. Response variable is most often denoted as y and also known as Dependent variable.
Covariance and Correlation are very helpful while understanding the relationship between two continuous variables. Covariance tells whether both variables vary in same direction (positive covariance) or in opposite direction (negative covariance). Whereas Correlation explains about the change in one variable leads how much proportion change in second variable.