2009, Data Collection, 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,

Modeling

Modeling Deep Learning Machine Learning Knowledge Discovery

ML internals: Synthetic Minority Oversampling (SMOTE) Technique

Domino Data Lab

MAY 20, 2021

Insufficient training data in the minority class — In domains where data collection is expensive, a dataset containing 10,000 examples is typically considered to be fairly large. Their tests are performed using C4.5-generated 1988), E-state data (Hall et al., The unreasonable effectiveness of data.

Machine Learning

Machine Learning Metrics Data mining Knowledge Discovery

Data Leaders Brief

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

ML internals: Synthetic Minority Oversampling (SMOTE) Technique

Webinars

Stay Connected