Think Logically
TTest is a statical hypothesis test, it is used for quantitative analysis. It is of two types,When we perform a t-test, we usually trying to find evidence of a significant difference between population means (2-sample t) or between the population mean and a hypothesized value (1-sample t). Thus in other words, 1 Sample: It will …
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 …
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) …
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 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−2logL^M and BIC(M)=klogn−2logL^MBIC(M)=klogn−2logL^M Note: where n is …
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 …