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Kindle Notes & Highlights
by
Peter Bruce
Read between
April 20 - May 10, 2019
Key Terms for Naive Bayes
Key Terms for Naive Bayes
Key Ideas
Key Ideas for Naive Bayes
Key Terms for Discriminant Analysis
Key Terms for Discriminant Analysis
Key Ideas for Discriminant Analysis
Key Ideas for Discriminant Analysis
Key Terms for Logistic Regression
Key Terms for Logistic Regression
Key Ideas for Logistic Regression
Key Ideas for Logistic Regression
Key Terms for Evaluating Classification Models
Key Terms for Evaluating Classification Models
Key Ideas for Evaluating Classification Models
Key Ideas for Evaluating Classification Models
Key Terms for Imbalanced Data
Key Terms for Imbalanced Data
Key Ideas for Imbalanced Data
Key Ideas for Imbalanced Data
Key Terms for K-Nearest Neighbors
Key Terms for K-Nearest Neighbors
Key Ideas for K-Nearest Neighbors
Key Ideas for K-Nearest Neighbors
Key Terms for Trees
Key Terms for Trees
Key Ideas
Key Ideas for Trees
Key Terms for Bagging and the Random Forest
Key Terms for Bagging and Random Forest
Key Ideas for Bagging and the Random Forest
Here
Key Terms for Boosting
Key Terms for Boosting
XGBoost Hyperparameters
XGBoost Hyperparameters
Key Ideas for Boosting
Key Ideas for Boosting
Key Ideas for Principal Components
Key Ideas for Principal Components
Key Terms for K-Means Clustering
Key Terms for K-Means Clustering
Key Ideas for K-Means Clustering
Key Ideas for K-Means Clustering
Key Terms for Hierarchical Clustering
Key Terms for Hierarchical Clustering
Key Ideas for Hierarchical Clustering
Key Ideas for Hierarchical Clustering
Key Ideas for Model-Based Clustering
Key Ideas for Model-Based Clustering
Key Terms for Scaling Data
Key Terms for Scaling Data
Key Ideas for Scaling Data
Key Ideas for Scaling Data
