2009, Deep Learning, Metrics and Predictive Modeling

Credit Card Fraud Detection using XGBoost, SMOTE, and threshold moving

Domino Data Lab

APRIL 21, 2021

deep learning) there is no guaranteed explainability. We will go through a typical ML pipeline, where we do data ingestion, exploratory data analysis, feature engineering, model training and evaluation. from sklearn import metrics. Learning from Imbalanced Data. from imblearn.over_sampling import SMOTE.

Statistics

Statistics Machine Learning Modeling Metrics

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

Domino Data Lab

AUGUST 1, 2021

The interest in interpretation of machine learning has been rapidly accelerating in the last decade. This can be attributed to the popularity that machine learning algorithms, and more specifically deep learning, has been gaining in various domains. Methods for explaining Deep Learning.

Modeling

Modeling Deep Learning Machine Learning Knowledge Discovery

Data Leaders Brief

Credit Card Fraud Detection using XGBoost, SMOTE, and threshold moving

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

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