# Monthly archives: January, 2018

## Support Vector Machine-SVM

A Support Vector Machine is a supervised algorithm that can classify cases by finding a separator. SVM works by first mapping data to a high dimensional feature space so that data points can be categorized, even when the data are not otherwise linearly separable. Then, a separator is estimated for the data. Lets X is …

## Activation Function and output

Lets create a matrix of shape 5×5 to understand the activation function working: import numpy as np Y2=np.arange(25).reshape(5,5) print(Y2) array([[ 0, 1, 2, 3, 4], [ 5, 6, 7, 8, 9], [10, 11, 12, 13, 14], [15, 16, 17, 18, 19], [20, 21, 22, 23, 24]]) Now create a weight matrix of similar shape W3=np.arange(25).reshape(5,5) …

## Parametric vs Non Parametric Algorithm

It depends on whether the number of parameters in the model grows as the number of examples in the dataset grows. Here, parameter means the entity that measures the model like median. Parametric :Fixed number of parameters regardless of the number of examples. Liner Regression Logistic Regression Linear Discriminant Analysis Perceptron Naive Bayes Non-Parametric: Not …

## AIC vs BIC

AIC stands for Akaike’s Information Criteria and BIC known for Bayesian Information Criteria. Akaike’s Information Criteria was formed in 1973 on the other hand Bayesian Information Criteria in 1978. When comparing the Bayesian Information Criteria and the Akaike’s Information Criteria, penalty for additional parameters is more in BIC than AIC. AIC(M)=2k−2logL^MAIC(M)=2k−2log⁡L^M and BIC(M)=klogn−2logL^MBIC(M)=klog⁡n−2log⁡L^M Note: where n is …

## Generalized Linear Models

Here general refers to the dependence on potentially more than one explanatory variable, v.s. the simple linear model : Some of the Generalized liner models are: 1-Ordinary Least Squares :Linear Regression fits a linear model with coefficients to minimize the residual sum of squares between the observed responses in the data set, and the responses …

## P Value

When you perform a hypothesis test in statistics, a p-value helps you determine the significance of your results. A small p-value (typically ≤ 0.05) indicates strong evidence against the null hypothesis, so you reject the null hypothesis. A large p-value (> 0.05) indicates weak evidence against the null hypothesis, so you fail to reject the …