Explainable AI (XAI) methods & Cheat Sheet
Explainable AI refers to methods and techniques that help humans understand and interpret the predictions and decisions made by machine…
Category: machine learning in a random forest
Enhancing Regression Models with Polynomial Features and L1 Lasso Regularization
Polynomial regression is a form of regression analysis in which the relationship between the independent variable and the dependent variable…
Understanding Common Types and Characteristics of Data
Analyzing various data types and characteristics enhances model efficiency, aiding in pattern recognition and informed decisions. An example of building a Predictive Model for Customer Churn is provided to illustrate this idea.
Key Roles of Transformation Matrices in Regression and PCA
Statistical Context: Projection and transformation matrices appear frequently in statistics, especially in regression and PCA, where they play a crucial…
Machine Learning and Deep Learning Free Online Courses
Basic probability & statistics Optimization & Background for Machine Learning and Deep Learning Deep learning: Introductory courses Advanced: Machine Learning…
Understanding Polynomial Regression: A Comprehensive Comic Guide with codes
Polynomial regression is a form of regression analysis in which the relationship between the independent variable and the dependent variable…
Understanding Random Forests for Quantile Prediction with sklearn implementation
Random forests enhance predictive performance by allowing quantile predictions, offering insights into outcome variability. This method is vital for risk assessment, aiding informed decision-making in uncertain environments.
Support Vector Machine + Python & R Codes
Support Vector Classifier (SVC) is a powerful algorithm for classification tasks, capable of handling linear and non-linear data using different kernel functions. It efficiently handles high-dimensional data for applications like image recognition and bioinformatics. Python and R codes demonstrate SVM usage for binary classification with breast cancer and mtcars datasets, respectively.
K-Means Clustering Method & Python Codes
K-Means Clustering is a popular unsupervised machine learning algorithm used for clustering data into groups. It is widely used in…
Logistic regression with L1 or L2 penalty with codes in Python and R
Logistic regression with L1 or L2 penalty adds regularization to prevent overfitting and improve model generalization. L1 penalty (Lasso) encourages sparsity in the model, making it suitable for datasets with many irrelevant features. L2 penalty (Ridge) retains all features with reduced importance. Python and R codes demonstrate implementation and evaluation of these regression techniques.