2009, Data Science, Knowledge Discovery and Testing

Explaining black-box models using attribute importance, PDPs, and LIME

Domino Data Lab

AUGUST 1, 2021

After forming the X and y variables, we split the data into training and test sets. Looking at the target vector in the training subset, we notice that our training data is highly imbalanced. PDPs for the bicycle count prediction model (Molnar, 2009). X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,

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ML internals: Synthetic Minority Oversampling (SMOTE) Technique

Domino Data Lab

MAY 20, 2021

The problem with this approach is that in highly imbalanced sets it can easily lead to a situation where most of the data has to be discarded, and it has been firmly established that when it comes to machine learning data should not be easily thrown out (Banko and Brill, 2001; Halevy et al., Their tests are performed using C4.5-generated

Machine Learning

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Explaining black-box models using attribute importance, PDPs, and LIME

ML internals: Synthetic Minority Oversampling (SMOTE) Technique

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