Machine learning
Machine Learning is a term that has been used in Mathematics, Statistics and Computer science for quite a few decades. It traces itself back almost for a century in Mathematics. The primary objective of Machine Learning is to make an inference about an even based on certain input values. Students of statistics could immediately relate to probabilistic determination of outcomes of events based on certain input parameters while students of Mathematics could identify this with linear algebra and matrix transformations.
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 … Read more
Probability vs Statistics Probability is used to predict the likelihood of a future event. vs Statistics is used to analyse 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 … Read more
In layman’s terms, Ridge regression adds one more term to linear regression’s cost function to reduce error. Ridge regression is a model-tuning method used to analyze data that suffer from multicollinearity. This method performs L2 regularization. When the issue of multicollinearity occurs, least-squares are unbiased, and variances are large; this results in predicted values being … Read more
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 … Read more
The decision tree is one of the most commonly used algorithms in supervised learning. It is used for both regression and classification when we use this algorithm for regression problems, it is called a regression tree. In real life, decision trees are very famous algorithms they are used to predict high occupancy dates for hotels, … Read more
“Polynomial Regression is a form of linear regression in which the relationship between the independent variable x and dependent variable y is modeled as an nth-degree polynomial. Polynomial regression fits a nonlinear relationship between the value of x and the corresponding conditional mean of y.” As stated in formal terms, this definition of polynomial regression … Read more
There are situations in daily life where we want to know the relationship between various factors, for example, if the price of petrol increases would it affect the sales of cars or does changing the location of the house will it affect the price. The process of finding this relationship between multitudes of factors is … Read more
K Nearest neighbour (KNN) is one of the most basic classification algorithms. It follows the assumption that similar things exist in close proximity to each other. The K-nearest neighbour algorithm calculates the distance between the various points whose category is known and then selects the shortest distance for the new data point. It is an … Read more
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 … Read more
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 … Read more
