Beginner to Advanced Guide on Machine Learning with R Tool

Explore cross-validation by random subsampling data into training and testing sets, fitting models, and evaluating accuracy across iterations. Estimate the average error rate using out-of-sample estimation and leave-one-out cross-validation.

Explains k-nearest neighbors classification using majority vote among neighbors, with Euclidean and Minkowski distances for numeric data and Hamming distance for categorical data, and notes standardization to handle mixed scales.

Implement a naive-bayes classifier in R for spam detection using text mining: import data, clean text, build corpus, create document-term matrix, convert to presence/absence, split data, train, and evaluate.

Load the forensic glass dataset from a package, perform linear discriminant analysis, view probabilities, group means, and discriminant coefficients, and predict classifications for new data.

Explore how artificial neural networks, a statistical learning model inspired by biological neurons, estimate functions from many inputs using interconnected neurons, weights, activation functions, and hidden layers.

Implement a neural network in R by preparing training and test partitions, training with neural net functions, and validating predictions on an infertility dataset.

Explore how back propagation, the backward propagation of errors, trains artificial neural networks by propagating error signals from outputs back to hidden layers to adjust weights via gradient descent.

Compare neural networks with back propagation, demonstrating an implementation on an inverter dataset with two hidden layers, and contrasting error rates and step counts.

Learn how support vector machines, a supervised learning method, use a hyperplane to separate data into two classes, maximize the margin, and operate in higher dimensional feature spaces.

Apply support vector machines in R by loading a dataset, splitting training and test sets, and experimenting with kernels such as RBF to evaluate model performance.

Learn how to implement a decision tree in R using a flowers dataset, splitting into training and testing sets, building and evaluating the model, and predicting new values.

Bootstrap aggregation, or bagging, improves accuracy by drawing multiple random training samples, training separate models on each, and averaging their predictions.

Boosting combines many predictors to reduce training error by iteratively focusing on misclassified points and summing weighted classifiers for improved predictions, using GBM with trees.

Explore how random forest builds an ensemble of decision trees for classification and regression, using random data subsets and majority voting to improve accuracy and handle missing data.

Explore how clustering groups objects by similarity, forming clusters that are more alike within than between. Compare two main approaches—k-means unsupervised learning and hierarchical clustering—and review their implementation.

Implement k-means clustering in R by loading the required package, performing a 70/30 training-testing split, and evaluating three clusters with their means and label comparisons.

Explore predicting with linear regression by modeling how the dependent variable changes when independent variables vary, using the regression equation and an error term.

Learn how to implement linear regression in R using the faithful dataset, fit a model with waiting time as the predictor for eruptions, and generate predictions and prediction intervals.

Predict binary, ordered, and multinomial outcomes using logistic regression, blending continuous and discrete predictors to model whether an event occurs rather than when.

Explains logistic regression on the S&P 500 daily returns (2001–2005) using lag1, lag2, and lag3 as predictors, with training before 2005, test on 2005, binomial link function, and 0.5 threshold.