Random Forest Classification in Python in 10 Lines

Random Forest algorithm is like an ensemble algorithm made of Decision Trees, which comprises more than one decision tree to create a model. It creates more than one tree like conditional control statements to create its model hence it is named as Random Forest. Random Forest machine learning algorithm can be used to solve both … Continue reading Random Forest Classification in Python in 10 Lines

Decision Tree Classification in Python in 10 lines

Decision tree machine learning algorithm can be used to solve not only regression but also classification problems. This algorithm creates a tree like conditional control statements to create its model hence it is named as decision tree. In this post we will be implementing a simple decision tree classification model using python and sklearn. First … Continue reading Decision Tree Classification in Python in 10 lines

Print ROC AUC Receiver Operating Characteristic Area Under Curve

The receiver operating characteristic area under curve is a way to measure the performance of a classification model, may be created using algorithms like Logistic Regression. ROC-AUC is basically a graph where we plot true positive rate on y-axis and false positive rate on x-axis. If a model is good the AUC will be close to 1. Area … Continue reading Print ROC AUC Receiver Operating Characteristic Area Under Curve

Linear Regression Synthetic Data using Make Regression

Though we have many datasets available on internet for implementing Linear Regression , many a times we may require to create a our own synthetic data. Scikit-Learn has a class called make regression , we can use this class to generate synthetic data for linear regression. from sklearn.datasets import make_regression # generate regression dataset x, … Continue reading Linear Regression Synthetic Data using Make Regression