Supervised Learning

In Supervised Learning, algorithms learn from labeled data. After understanding the data, the algorithm determines which label should be given to new data based on pattern and associating the patterns to the unlabeled new data.

e two tasks of supervised learning: regression and classification

Regression: predict a continuous numerical value. How much will that house sell for?

Classification: assign a label. Is this a picture of a cat or a dog?

Classification: Classification is a technique for determining class the dependent belongs to based on the one or more independent variables.

Classification is used for predicting discrete responses.

1.Logistic Regression

Logistic regression is kind of like linear regression but is used when the dependent variable is not a number

2.K-Nearest Neighbors (K-NN)

K-NN algorithm is one of the simplest classification algorithm and it is used to identify the data points that are separated into several classes to predict the classification of a new sample point.

3.Support Vector Machine (SVM)

Support Vector is used for both regression and Classification. It is based on the concept of decision planes that define decision boundaries. Types of SVM:

·        Kernel SVM

·        Radial Basis Function(RBF) Kernel

4.Naive Bayes

Naive Bayes classifier is based on Bayes’ theorem with the independence assumptions between predictors 

Author: Divyanshu singh

Mentor-Vishwa Prabhakar Singh