Machine Learning models are mainly used for two tasks such as classification task and regression task. A classification problem is about predicting a discrete class label and regression task is prediction a continuous quantity.
Classification
The classification problem can be a binary classification or multiclass classification.
 A problem corresponding two class is called binary classification.
 A problem corresponding to multiple class is called multiclass classification.
Generally, the classification model predicts the probability of each class for a given input. The probability can be interpreted is a likelihood of a given input to each class. The highest probability class can be considered as a predicted class label of a given input.
Example of binary Classification task:
 Predict the email is spam or not spam.
 Predict the image contain dog or cat.
 Predict the voice of female or male.
 predict the person has cancer or not.
Example of multiclass classification task:
 Predict the emotion of a person such as sad, happy, neutral, angry.
 Predict the digit (09) from a digit image.
 Predict the image of a dress into a different pattern
 Predict the sentiment of a text.
Regression
Regression predictive is a task of predicting a continuous value of given input variables. A continuous output variable can be an integer or floatingpoint value.
Example:
 Predict the house price from various house features such as area, the number of bedrooms, built year, etc.
 Predict the share market movements.
 Predict the quantity of rain by analysis of the weather.
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Machine Learning Model
There are various machine learning algorithms exists to solve the classification and regression problems. These model are mainly classified into two categories such as treebased algorithm and nontree based algorithm.
Treebased algorithm:
Treebased algorithms have a powerful treelike structure which split the space into subspace. Treebased algorithms have a nonlinear relationship with its features. They use a divide and conquer approach for splitting. This kind of algorithms have multiple trees and the final outcome is made by combining trees. Below is the list of treebased algorithms:
 Decision Tree
 Random Forest
 Gradient Boosting Tree
NonTree based algorithm:
An Algorithm is called nontree based algorithm which doesn’t have a treelike structure. Below is the list of nontree based algorithms:
 Linear Model: It is a very simple approach. It split space into two subspaces.

 Logistic Regression
 Support Vector Machine (SVM)

 Neural Networks (NN) : It has a very complex structure. It produces the nonlinear decision boundary.
 KNearest Neighbour (KNN) : It makes groups of high similarity data points and heavily rely on measurement between points.
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