Notes / Resources on Machine Learning
Creating this page to keep track of some helpful machine learning resources that I come across and to remind myself of some concepts as I try to complete some machine learning projects
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Imputation of Missing Values
For various reasons, many real world datasets contain missing values, often encoded as blanks, NaNs, or other placeholders. Such datasets are incompatible with scikit-learn estimators which assume that all values in an array are numerical, and that all have and hold meaning.
- You could remove the rows of data or impute them - infer them from known parts of data.
- You can either impute values only by using values from that column - SimpleImputer - or you could use multivariate imputation algorithms to use the entire set of available feature dimensions to estimate the missing values - IterativeImputer
- SimpleImputer allows you to replace missing values with a provided constant value or using statistics from that column (mean, median, or most frequent)
- KNNImputer provides imputation for filling in missing values using the k-Nearest-Neighbors approach.
Cross Validation
- Something you need to review
Precision vs Recall of Classifiers
- Something you need to review
Bagging and Pasting
- Something you need to review
Kernel Trick
- Something you need to review
Random Forests
- Something you need to review
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