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

Scikit Learn Source

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

Notes / Resources on Machine Learning

Imputation of Missing Values

Cross Validation

Precision vs Recall of Classifiers

Bagging and Pasting

Kernel Trick

Random Forests

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