Hits: 17 A Practical approach to Simple Linear Regression using R Simple Linear Regression is a statistical method that allows us to summarize and study relationships between two continuous (quantitative) variables. One variable denoted x is regarded as an independent variable and other one denoted y is regarded as a dependent variable. It is …

Hits: 21 Locally weighted Linear Regression Linear regression is a supervised learning algorithm used for computing linear relationships between input (X) and output (Y). The steps involved in ordinary linear regression are: Training phase: Compute to minimize the cost. Predict output: for given query point , As evident from the image below, this algorithm cannot be used for …

Hits: 11 Simple Linear-Regression using R Linear Regression : It is a commonly used type of predictive analysis. It is a statistical approach for modelling relationship between a dependent variable and a given set of independent variables. There are two types of linear regression. Simple Linear Regression Multiple Linear Regression Let’s discuss Simple …

Hits: 62 End-to-End Machine Learning: Boston House Price Prediction in R Boston House Price Prediction is a machine learning task that involves predicting the median value of owner-occupied homes in Boston, Massachusetts, based on certain characteristics such as the number of rooms, the crime rate, and the distance to employment centers. Understanding the value of …

Hits: 58 End-to-End Machine Learning: Abalone Prediction in R Abalone prediction is a machine learning task that involves identifying the age of an abalone, which is a type of sea snail, based on certain characteristics such as the abalone’s length, diameter, height, and weight. Understanding the age of the abalone can be useful for both …

Hits: 48 Evaluate Machine Learning Algorithm – repeated kfold cross validation in R Evaluating the performance of a machine learning algorithm is an important step in understanding how well it will work on new, unseen data. One popular method for evaluating the performance of an algorithm is called “repeated k-fold cross validation.” In repeated k-fold …

Hits: 96 Regression with CARET in R Regression is a type of machine learning that is used to predict a continuous variable based on one or more input variables. CARET (short for “Classification And REgression Training”) is a powerful tool in R for training and comparing different regression algorithms. When using CARET for regression, you …

Hits: 183 Classification in R – Bagging CART in R Classification is a type of supervised machine learning that is used to predict the class or category of a new observation based on the values of its predictors. One popular method of classification is using bagging (also known as bootstrap aggregating) with Classification and Regression …

Hits: 58 Support Vector Machine in R Support Vector Machine (SVM) is a type of supervised machine learning algorithm that can be used for both classification and regression tasks. It works by finding the best boundary, called a hyperplane, that separates different classes or predicts the target variable with the highest accuracy. In R, there …

Hits: 43 Regression with classification and regression trees in R Regression with classification and regression trees (CART) is a type of statistical analysis that is used to model relationships between variables. It is a decision tree-based algorithm that can be used for both classification and regression tasks. It’s a tree-based method, where the algorithm recursively …

Hits: 50 Non-Linear Regression in R – regression with bagging_CART in R Non-linear regression is a type of statistical analysis that is used to model relationships between variables that are not linear. In other words, it is used to model relationships where the change in one variable is not directly proportional to the change in …

Hits: 60 Non-Linear Regression in R – random forest algorithm in R Non-linear regression is a type of statistical analysis that is used to model relationships between variables that are not linear. In other words, it is used to model relationships where the change in one variable is not directly proportional to the change in …