End-to-End Machine Learning: accuracy metric in R When training a machine learning model, it’s important to evaluate its performance to understand how well it will work on new, unseen data. One common way to evaluate the performance of a model is by using a metric called “accuracy.” Accuracy is a measure of how often the …
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 cross validation, …
Evaluate Machine Learning Algorithm – leave one out 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 “leave-one-out cross validation” (LOOCV). In leave-one-out cross validation, …
Evaluate Machine Learning Algorithm in R – 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 “k-fold cross validation.” In k-fold cross validation, the …
Evaluate Machine Learning Algorithm in R – dataset split 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 common method for evaluating the performance of an algorithm is to split the available data into two sets: a training …
Evaluate Machine Learning Algorithm in R – bootstrap 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 “bootstrapping.” Bootstrapping is a resampling method that creates multiple new …
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 start by …
Machine Learning with CARET in R – Binary Classification with CARET in R CARET (short for “Classification And REgression Training”) is a powerful tool in R for training and comparing machine learning algorithms. One of the most common tasks in machine learning is binary classification, which is the process of sorting items into one of …
Algorithm Checkpoint with CARET in R CARET (short for “Classification and REgression Training”) is a powerful tool in R for training and comparing different machine learning algorithms. It provides a consistent and easy-to-use interface for working with many different algorithms, including decision trees, random forests, support vector machines (SVMs), and more. One of the most …
Classification in R – SVM in R Support Vector Machine (SVM) is a popular method for classification in machine learning and data analysis. It’s a type of algorithm that can sort items into different categories based on their characteristics. An SVM works by finding the best boundary, or “decision boundary,” that separates the different classes …