Sequence Model

 Adjusted R square penalizes attribute that are not correlated

In probability covariance is the measure of the joint probability for two random variables. It describes how the two variables change together  It is denoted as the function cov(X,Y) Where X and Y are the two random variables being considered  cov(X,Y) Covariance is calculated as the expected value or average of the product of the differences of each variable from theor expectedvalues ,Where E[X] is the expected value for X and E(Y) is the expected value of y. in simple terms  cov(X,Y)=E[ ( X - E[X] ) . ( Y - E[Y] ) ] for n values  cov(X,Y)=sum(E[ ( X - E[X] ) . ( Y - E[Y] ) ]) * 1/n or, cov(X,Y)=sum([ ( X - X^ ) . ( Y - Y^ ) ]) * 1/n  sum is upto n The sign indicates that whether two variables increase together or decrease together. +ve sign means value are increasing and -ve means value are decreasing  A variance value of zero are completely indicated that both variables are independent in numpy we use conv()  to find covariance Note. It doesn't show...

Linear Regression ,intuitively is a regression algorith with alinear approach. We try to predict a continuous value of a given data point by generealising on the data that we have in hand . The linear part indicate that we are using linear approach in generalising over the data There are two types of multiple linear  Regression: ordinary least squares and generalized least squares (GLS) . THe mian difference between the two is that OLS assumes there is not a strong correlation between any two independent variables GLS deals with correlated independent variables by transforming the data and then using OLS to build the model with transformed data  What are the basic assumption.?  There are four assumption associated with a linear regression model. 1->Linearity:-The relationship between X and the mean of Y is Linear. 2->Homoscedasticity:-The variance of residual is the same for any value of X 3->Independence:-Observations are Independent of each other  4->No...

It Creates hyperplane between the points so that it can be differentiable that's why it is known as classification algorithm  It works on smaller datasets ,but on the complex ones it can be much stronger and powerfull in building macine Learning Models  It can be Used for both classification and regression problems  But mostly it is used in Classification algorithms . In this we plot each data item as a point in n dimensional space (where n is number of features you have) with the value of each feature being the value  of a particular coordinate . Support Vectors are basically the co-ordinates of individual observation. The SVM classifier  is a fromtier which segregates the two classes (Hyper plane / line) Now How can we Identify the right Hyperplane : Identify the right hyperplane (Scenario-1)- Here we have three hyper planes (A,B and C) Now identify the right Hyperplane to classify star and circle  Hyperplane B will seggregate be treated as Hyperplane...