Data Mining: Practical Machine Learning Tools and Techniques, chapter 6. Summary. In this post you have discovered the Classification And Regression Trees (CART) for machine learning. You learned: The classical name Decision Tree and the more Modern name CART for the algorithm. The representation used for CART is a binary tree.
K-means is one of the simplest and the best known unsupervised learning algorithms, and can be used for a variety of machine learning tasks, such as detecting abnormal data, clustering of text documents, and analysis of a dataset prior to using other classification or regression methods.
Tutorial To Implement k-Nearest Neighbors in Python From Scratch Below are some good machine learning texts that cover the KNN algorithm from a predictive modeling perspective. Applied Predictive Modeling, Chapter 7 for regression, Chapter 13 for classification.
Logistic regression is another technique borrowed by machine learning from the field of statistics. It is the go-to method for binary classification problems (problems with two class values). In this post you will discover the logistic regression algorithm for machine learning.
Random Forest is one of the most popular and most powerful machine learning algorithms. It is a type of ensemble machine learning algorithm called Bootstrap Aggregation or bagging. In this post you will discover the Bagging ensemble algorithm and the Random Forest algorithm for predictive modeling.
“Support Vector Machine” (SVM) is a supervised machine learning algorithm which can be used for both classification or regression challenges. However, it is mostly used in classification problems. In this algorithm, we plot each data item as a point in n-dimensional space (where n is number of features you have) with the value of each ...