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Jeff Nirschl 036d5e4c5f
Update feature engineering to bin/quantize the continuous features Age, Fare, and family_size.Update parameter tuning to allow hyperopt to optimize n_estimators for RF model. Also, change hyperopt.hp.choice to hyperopt.hp.quniform for sampling integer features from a uniform distribution. Previously, hp.quniform was returning float values when the RF model required integers. The workaround included adding the following to the objective function: param[key] = int(param[key])
3 years ago
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afecec1ddb
initial commit using cookiecutter data science
3 years ago
afecec1ddb
initial commit using cookiecutter data science
3 years ago
036d5e4c5f
Update feature engineering to bin/quantize the continuous features Age, Fare, and family_size.Update parameter tuning to allow hyperopt to optimize n_estimators for RF model. Also, change hyperopt.hp.choice to hyperopt.hp.quniform for sampling integer features from a uniform distribution. Previously, hp.quniform was returning float values when the RF model required integers. The workaround included adding the following to the objective function: param[key] = int(param[key])
3 years ago
49cd29a4db
Add placeholder script build_features.py to allow feature engineering (currently just saves a copy of the input dataframe as "_featurized.csv"). Add DVC stage build_features (feature engineering) prior to feature normalization. Run DVC stages build_features, normalize_data, and split_train_dev with all stages working. Update README.md to include feature engineering stage.
3 years ago

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