In classification, the target variable is categorical and unordered.. To solve a multi-class classification problem, we typically choose one of the following supervised learning algorithms in machine ..
For Classification Problems In classification, the target variable is categorical and unordered. To solve a two-class classification ..
In classification, the target variable is categorical and unordered.. To solve a two-class classification problem, we typically ..
In regression, the target variable is continuous or ordered whole values. To solve a regression problem, we typically choose ..
Reinforcement Learning a the 3rd area in Machine Learning. It’s different from Supervised Learning and Unsupervised Learning. ..
Overfitting (aka. high variance) If we have too many features, the learned hypothesis may fit the training set very well ..
Logistic regression, despite its name, is a linear model for classification rather than regression. It is regressing for the probability ..
Polynomial regression is a form of linear regression in which the relationship between the independent variable x and the dependent ..
This blog talks about when you should use Gradient Descent and when you should use Normal Equation. Here are some of their ..
Linear Regression with Multiple Variables, also called as ‘Multivariate Linear Regression’, is used when you want to predict ..
Linear Regression with One Variable, also called as ‘Univariate Linear Regression’, is used when you want to predict ..
Regression In regression, the target variable is continuous or ordered whole values For example, suppose you are working ..
Supervised Learning Supervised learning (machine learning) takes a known set of input data and known(labeled) responses to ..