Course Wiki

Explore more — Machine Learning Zoomcamp Module 3

Re-implement train/val/test split using scikit-learn in the project from the last week Also, instead of our own linear regression, use LinearRegression (not regularized) and RidgeRegression (regularized). Find the best regularization...

Machine Learning Zoomcamp › Module 3: Machine Learning for Classification › Explore more

Notes

Explore more

More things

• Try to exclude least useful features

Use scikit-learn in project of last week

• Re-implement train/val/test split using scikit-learn in the project from the last week
• Also, instead of our own linear regression, use LinearRegression (not regularized) and RidgeRegression (regularized). Find the best regularization parameter for Ridge
• There are other ways to implement one-hot encoding. E.g. using the OneHotEncoding class. Check how to use it here.
• Sometimes numerical features require scaling, especially for iterative solves like “lbfgs”. Check how to use StandardScaler for that here.

Other projects

• Lead scoring - https://www.kaggle.com/ashydv/leads-dataset
• Default prediction - https://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients

Key concepts

• Regularization
• Linear Regression
• scikit-learn

Related notes

• mlz-m01-setting-up-the-environment
• mlz-m01-summary
• mlz-m02-baseline-model-for-car-price-prediction-project
• mlz-m02-car-price-prediction-project
• mlz-m02-car-price-prediction-project-summary
• mlz-m02-explore-more
• mlz-m02-linear-regression
• mlz-m02-linear-regression-vector-form
• mlz-m02-regularization
• mlz-m02-training-linear-regression-normal-equation
• mlz-m02-tuning-the-model
• mlz-m03-churn-prediction-project
• mlz-m03-data-preparation
• mlz-m03-eda
• mlz-m03-feature-importance-churn-rate-and-risk-ratio
• mlz-m03-feature-importance-correlation
• mlz-m03-feature-importance-mutual-information
• mlz-m03-logistic-regression
• mlz-m03-model-interpretation
• mlz-m03-one-hot-encoding
• mlz-m03-setting-up-the-validation-framework
• mlz-m03-summary
• mlz-m03-training-logistic-regression-with-scikit-learn
• mlz-m03-using-the-model
• mlz-m04-accuracy-and-dummy-model
• mlz-m04-cross-validation
• mlz-m04-roc-auc
• mlz-m04-roc-curves
• mlz-m05-python-virtual-environment-pipenv
• mlz-m06-decision-tree-learning-algorithm
• mlz-m06-decision-trees
• mlz-m06-ensemble-learning-and-random-forest
• mlz-m06-xgboost-parameter-tuning
• mlz-m08-fashion-classification
• mlz-m08-regularization-and-dropout
• mlz-m09-explore-more

Sources

• Lesson file