We use different classification algorithms to build classifiers in machine learning. The naive Bayes classification algorithm is one of the easiest classification algorithms to understand and implement. In this article, we will discuss the Bayes algorithm and the intuition of Naive Bayes classification. We will also discuss a numerical example of Naive Bayes classification to…

Overfitting and Underfitting are two of the common issues that we face while training machine learning models. In this article, we will discuss overfitting and underfitting in machine learning. We will also discuss how to avoid overfitting and underfitting using various techniques.  Before we start with a discussion on overfitting and underfitting, I suggest you…

When we train machine learning models, we might not always get very good results. We often run into situations where overfitting or underfitting occurs due to which the performance of the machine learning model deteriorates. This happens due to bias and variance errors. In this article, we will discuss these errors in machine learning and…

We often use categorical data encoding techniques such as label encoding and one hot encoding while data preprocessing. While these techniques offer an easy solution to convert categorical data to a numeric format, the representations are often inaccurate. In this article, we will discuss how to perform entity embedding to convert categorical data into a…

We use different categorical data encoding techniques while data analysis and machine learning tasks. In this article, we will discuss the basics of one hot encoding. We will also discuss implementing one hot encoding in Python. What is One Hot Encoding? One hot encoding is an encoding technique in which we represent categorical values with…