What is Confusion Matrix in Machine Learning

Not only human beings but also the machine learning models may get confused !! After all Artificial Intelligence mimics a human brain, isn’t it?(pun intended). Imagine yourself as a machine learning engineer and suppose you trained a machine learning classification model successfully today. After the model is trained you checked the accuracy which is 93.0%. Wow … Continue reading What is Confusion Matrix in Machine Learning

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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