Let’s talk about Precision and Recall in today’s article. Whenever we implement a classification problem (i.e decision trees) to classify data points, there are points that are often misclassified. Even though accuracy gives a general idea about how good the model is, we need more robust metrics to evaluate our model. Let’s consider an Example. […]
The ROC curve stands for Receiver Operating Characteristic curve. ROC curves display the performance of a classification model. ROC tells us how good the model is for distinguishing between the given classes, in terms of the predicted probability. In this article, we will understand ROC curves, what is AUC, and implement a binary classification problem […]
In this article, we’ll learn to implement K-Nearest Neighbors from Scratch in Python. KNN is a Supervised algorithm that can be used for both classification and regression tasks. KNN is very simple to implement. In this article, we will implement the KNN algorithm from scratch to perform a classification task. The intuition behind the K-Nearest […]
In this article, we’ll learn to implement Linear regression from scratch using Python. Linear regression is a basic and most commonly used type of predictive analysis. It is used to predict the value of a variable based on the value of another variable. The variable we want to predict is called the dependent variable. The […]
K-Means is a very popular clustering technique. The K-means clustering is another class of unsupervised learning algorithms used to find out the clusters of data in a given dataset. In this article, we will implement the K-Means clustering algorithm from scratch using the Numpy module. The 5 Steps in K-means Clustering Algorithm Step 1. Randomly […]
In the field of Natural language processing, Stemming and Lemmatization are the text normalization techniques used to prepare texts, documents for further analysis. Understanding Stemming and Lemmatization While working with language data we need to acknowledge the fact that words like ‘care’ and ‘caring’ have the same meaning but used in different forms of tenses. […]
