Precision, recall, and f1-score are very popular metrics in the evaluation of a classification algorithm. It is very easy to calculate them using libraries or packages nowadays. But I believe…
Linear regression is the most basic type of machine learning. It is based on the simple straight-line formula we have all learned in middle school. Though there are a lot…
Outliers can be a big problem in data analysis or machine learning. Only a few outliers can totally alter a machine learning algorithm's performance or totally ruin a visualization. So,…
Null values are a big problem in machine learning and deep learning. If you are using sklearn, TensorFlow, or any other machine learning or deep learning packages, it is required…
The decision tree is a very popular machine learning algorithm. It works for both linear and non-linear data. Also, it can be used for both classification and regression. With great…
The meaning of the word regularization is “the act of changing a situation or system so that it follows laws or rules”. That’s what it does in the machine learning world…
Performance evaluation is the most important part of machine learning in my opinion. Because machine learning itself has become pretty easy because of all the libraries and packages. Anyone can…
Feature selection is one of the most important parts of machine learning. In most datasets in the real world, there might be many features. But not all the features are…
Stochastic gradient descent is a widely used approach in machine learning and deep learning. This article explains stochastic gradient descent using a single perceptron, using the famous iris dataset. I…
One of the most basic, popular, and powerful statistical models is logistic regression. If you are familiar with linear regression, logistic regression is built upon linear regression. It uses the…