diff --git a/mlcourse_ai_jupyter_book/book/topic04/topic04_intro.md b/mlcourse_ai_jupyter_book/book/topic04/topic04_intro.md index 6dea9e519..74d0a5915 100644 --- a/mlcourse_ai_jupyter_book/book/topic04/topic04_intro.md +++ b/mlcourse_ai_jupyter_book/book/topic04/topic04_intro.md @@ -27,8 +27,8 @@ The following 5 articles may form a small brochure, and that's for a good reason - the [theory](https://youtu.be/ne-MfRfYs_c) behind linear models, an intuitive explanation; - [business case](https://youtu.be/B8yIaIEMyIc), where we discuss a real regression task – predicting customer Life-Time Value; -4\. Complete [demo assignment 4](assignment04) (same as a [Kaggle Notebook](https://www.kaggle.com/kashnitsky/a4-demo-sarcasm-detection-with-logit)) on sarcasm detection; +4\. Complete [demo assignment 4](assignment04) (same as a [Kaggle Notebook](https://www.kaggle.com/code/kashnitsky/a6-demo-linear-models-and-rf-for-regression)) where you explore OLS, Lasso and Random Forest in a regression task; -5\. Check out the [solution](assignment04_solution) (same as a [Kaggle Notebook](https://www.kaggle.com/kashnitsky/a4-demo-sarcasm-detection-with-logit-solution)) to the demo assignment (optional); +5\. Check out the [solution](assignment04_solution) (same as a [Kaggle Notebook](https://www.kaggle.com/code/kashnitsky/a6-demo-regression-solution)) to the demo assignment (optional); 6\. Complete [Bonus Assignment 4](https://www.patreon.com/ods_mlcourse) where you'll be guided through working with sparse data, feature engineering, model validation, and the process of competing on Kaggle. The task will be to beat baselines in that ["Alice" Kaggle competition](https://www.kaggle.com/c/catch-me-if-you-can-intruder-detection-through-webpage-session-tracking2). That's a very useful assignment for anyone starting to practice with Machine Learning, regardless of the desire to compete on Kaggle (optional, available under Patreon ["Bonus Assignments" tier](https://www.patreon.com/ods_mlcourse)). diff --git a/mlcourse_ai_jupyter_book/book/topic04/topic4_linear_models_part5_valid_learning_curves.md b/mlcourse_ai_jupyter_book/book/topic04/topic4_linear_models_part5_valid_learning_curves.md index 74c74fd33..34e674047 100644 --- a/mlcourse_ai_jupyter_book/book/topic04/topic4_linear_models_part5_valid_learning_curves.md +++ b/mlcourse_ai_jupyter_book/book/topic04/topic4_linear_models_part5_valid_learning_curves.md @@ -78,21 +78,21 @@ y = data["Churn"].astype("int").values X = data.drop("Churn", axis=1).values ``` -**We will train logistic regression with stochastic gradient descent. Later in the course, we will have a separate article on this topic.** +**We will train an SVM with stochastic gradient descent. Later in the course, we will have a separate article on this topic.** ```{code-cell} ipython3 -alphas = np.logspace(-2, 0, 20) -sgd_logit = SGDClassifier(loss="log", n_jobs=-1, random_state=17, max_iter=5) +alphas = np.logspace(-4, 0, 20) +sgd_model = SGDClassifier(loss="hinge", n_jobs=-1, random_state=17) logit_pipe = Pipeline( [ ("scaler", StandardScaler()), ("poly", PolynomialFeatures(degree=2)), - ("sgd_logit", sgd_logit), + ("sgd_model", sgd_model), ] ) val_train, val_test = validation_curve( - estimator=logit_pipe, X=X, y=y, param_name="sgd_logit__alpha", param_range=alphas, cv=5, scoring="roc_auc" + estimator=logit_pipe, X=X, y=y, param_name="sgd_model__alpha", param_range=alphas, cv=5, scoring="roc_auc" ) ``` @@ -123,12 +123,10 @@ plt.grid(True); The trend is quite visible and is very common. -- For simple models, training and validation errors are close and large. This suggests that the model **underfitted**, meaning it does not have a sufficient number of parameters. +- For simple models, training and validation errors are close and large (conversely, metrics like ROC AUC or accuracy are low). This suggests that the model **underfitted**, meaning it does not have a sufficient number of parameters. - For highly sophisticated models, training and validation errors differ significantly. This can be explained by **overfitting**. When there are too many parameters or regularization is not strict enough, the algorithm can be "distracted" by the noise in the data and lose track of the overall trend. - - ### How much data is needed? The more data the model uses, the better. But how do we understand whether new data will helpful in any given situation? 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