## Machine Learning – Classification

Classification Naive Bayes Naive Bayes is a classification algorithm which checks the probability of a test point belonging to a class. Unlike other algorithms, Naive Bayes is purely a classification algorithm. As explained in the last release this is the formula for the Bayes theorem and as the algorithm name suggests Naive Bayes is inspired…

## Random forest

In supervised learning, overfitting is a major problem. When it comes to regression trees, it becomes more obvious to prevent this, we specify the Seth of the tree, but there is still a high chance of the dataset being overfitted. To avoid this, we use a random first regressor. Before understanding how and why this…

## Naive Bayes

Naive Bayes is a classification algorithm which checks the probability of a test point belonging to a class. Unlike other algorithms, Naive Bayes is purely a classification algorithm. As explained in the last release this is the formula for the Bayes theorem and as the algorithm name suggests Naive Bayes is inspired by the Bayes…

## Conditional Probability

Probability vs. Statistics Probability is used to predict the likelihood of a future event. vs Statistics are used to analyze past events Basics of Probability Probability, in simple terms, is the likelihood of a situation happening. When unsure of the outcome, the probability can be calculated to know its chances. Probability(Event)=(Number of favourable outcomes of…

## Activation Function: Sigmoid

What is an activation function? Activation functions get their names from being used in neural networks as they decide whether a particular neuron should be activated. In the context of this release, the sigmoid function will be discussed in detail as it is used in the logistic regression algorithm for binary classification.  Sigmoid Activation function…

## Multi-Class Classification

In machine learning, classification is the method of classifying data using certain input variables. A dataset with labels given (training dataset) is used to train the model in a way that the model can provide labels for datasets that are not yet labeled. Under classification, there are 2 types of classifiers: Binary Classification Multi-Class Classification…

## Binary Classification

In machine learning, classification is classifying data using certain input variables. A dataset with labels given (training dataset) is used to train the model in a way that the model can provide labels for datasets that are not yet labeled. Under classification, there are 2 types of classifiers: Binary Classification Multi-Class Classification Here let’s discuss…

## Need for different types of Classifiers

Classification algorithms depend on the dataset being used, and data scientists have curated various algorithms that can be used in certain situations. Most popular types of Classification Algorithms: Linear Classifiers Logistic regression Naive Bayes classifier Support vector machines Kernel estimation k-nearest neighbor Decision trees Random forests Let’s discuss this in more detail and understand when…

## Introduction to Classification

Under supervised learning, there is a type called classification. These algorithms recognize the category a new observation belongs to based on the training dataset. In supervised learning, there are independent variables and a dependent variable Here, the dependent variable is the category, and each category’s features are independent variables. These categories are distinct and pre-defined…