add under/over-fitting article by simiion12 · Pull Request #19 · SigmoidAI/articles

simiion12 · 2024-03-13T10:29:29Z

In this article you can learn about how to deal with underfitting and overfitting in classification models.

eduard-balamatiuc · 2024-11-19T15:27:45Z

article-under-overfitting/article.md

+
+## Underfitting
+
+Conversely, underfitting in classification models arises when the model fails to capture the underlying patterns present in the data adequately. This deficiency often stems from the use of overly simplistic models or inadequate model training. For instance, employing linear classifiers in scenarios with nonlinear decision boundaries may result in underfitting, leading to suboptimal classification performance. Furthermore, underfitting can be exacerbated by factors such as feature scaling, imbalanced class distributions, or insufficient model complexity. In such cases, the model may struggle to discern meaningful patterns, resulting in poor predictive accuracy across both training and validation datasets. Addressing underfitting requires careful consideration of model selection, feature engineering, and optimization techniques to ensure that the model can effectively capture the complexity of the classification task while avoiding unnecessary bias or simplification.Conversely, underfitting in classification models arises when the model fails to capture the underlying patterns present in the data adequately. This deficiency often stems from the use of overly simplistic models or inadequate model training. For instance, employing linear classifiers in scenarios with nonlinear decision boundaries may result in underfitting, leading to suboptimal classification performance. Furthermore, underfitting can be exacerbated by factors such as feature scaling, imbalanced class distributions, or insufficient model complexity. In such cases, the model may struggle to discern meaningful patterns, resulting in poor predictive accuracy across both training and validation datasets. Addressing underfitting requires careful consideration of model selection, feature engineering, and optimization techniques to ensure that the model can effectively capture the complexity of the classification task while avoiding unnecessary bias or simplification.


Suggested change

Conversely, underfitting in classification models arises when the model fails to capture the underlying patterns present in the data adequately. This deficiency often stems from the use of overly simplistic models or inadequate model training. For instance, employing linear classifiers in scenarios with nonlinear decision boundaries may result in underfitting, leading to suboptimal classification performance. Furthermore, underfitting can be exacerbated by factors such as feature scaling, imbalanced class distributions, or insufficient model complexity. In such cases, the model may struggle to discern meaningful patterns, resulting in poor predictive accuracy across both training and validation datasets. Addressing underfitting requires careful consideration of model selection, feature engineering, and optimization techniques to ensure that the model can effectively capture the complexity of the classification task while avoiding unnecessary bias or simplification.Conversely, underfitting in classification models arises when the model fails to capture the underlying patterns present in the data adequately. This deficiency often stems from the use of overly simplistic models or inadequate model training. For instance, employing linear classifiers in scenarios with nonlinear decision boundaries may result in underfitting, leading to suboptimal classification performance. Furthermore, underfitting can be exacerbated by factors such as feature scaling, imbalanced class distributions, or insufficient model complexity. In such cases, the model may struggle to discern meaningful patterns, resulting in poor predictive accuracy across both training and validation datasets. Addressing underfitting requires careful consideration of model selection, feature engineering, and optimization techniques to ensure that the model can effectively capture the complexity of the classification task while avoiding unnecessary bias or simplification.

Conversely, underfitting in classification models arises when the model fails to capture the underlying patterns present in the data adequately. This deficiency often stems from the use of overly simplistic models or inadequate model training. For instance, employing linear classifiers in scenarios with nonlinear decision boundaries may result in underfitting, leading to suboptimal classification performance. Furthermore, underfitting can be exacerbated by factors such as feature scaling, imbalanced class distributions, or insufficient model complexity. In such cases, the model may struggle to discern meaningful patterns, resulting in poor predictive accuracy across both training and validation datasets. Addressing underfitting requires careful consideration of model selection, feature engineering, and optimization techniques to ensure that the model can effectively capture the complexity of the classification task while avoiding unnecessary bias or simplification.

eduard-balamatiuc · 2024-11-19T15:38:53Z

article-under-overfitting/article.md

+3- Elatic Net
+
+    from sklearn.linear_model import ElasticNet
+    from sklearn.datasets import make_regression
+
+    # Elastic Net regularization
+    X, y = make_regression(n_features=2, random_state=0)
+    elastic_net = ElasticNet(random_state=0, alpha=1.0, l1_ratio=0.5)
+    elastic_net.fit(X, y)


I believe Elastic Net is a regression model, does it fit here for the classification task?
https://scikit-learn.org/1.5/modules/linear_model.html#elastic-net

eduard-balamatiuc · 2024-11-19T15:40:17Z

article-under-overfitting/article.md

+    feture_importances = feature_importances.head(10)
+
+    # Plot the feature importances
+    feture_importances.plot(kind='barh')
+    plt.title('Feature Importances')


was the typo feture intentional?

eduard-balamatiuc · 2024-11-19T15:41:33Z

article-under-overfitting/article.md

+    import matplotlib.pyplot as plt
+
+    from sklearn.ensemble import RandomForestClassifier
+    feature_importances = df(clf_forest.feature_importances_, index=X.columns, columns=['importance']).sort_values('importance', ascending=False)


there is no prior definition for the clf_forest can you make sure to include all the definitions so that the reader could replicate the code

eduard-balamatiuc · 2024-11-19T15:43:23Z

article-under-overfitting/article.md

+    from sklearn.preprocessing import PolynomialFeatures
+    from sklearn.linear_model import LinearRegression
+    from sklearn.pipeline import make_pipeline
+
+    # Sample code to create a polynomial regression model
+    degree = 7  # The degree of the polynomial features
+    polyreg = make_pipeline(PolynomialFeatures(degree), LinearRegression())


Here you are once again using a LinerRegression model for the classification article, was that intentional?

eduard-balamatiuc · 2024-11-19T15:44:56Z

article-under-overfitting/src/photos/overfitting1.png

can you also provide some code for generating this graphs, especially if they were generated on the data that you worked with

eduard-balamatiuc · 2024-11-19T15:53:19Z

article-under-overfitting/article.md

you might also want to consider methods that specifically refer or address the imbalance like SMOTE or ADASYN or other ones available

eduard-balamatiuc · 2024-11-19T15:53:59Z

article-under-overfitting/article.md

+### Other tips for overfitting:
+1) Use ensemble techniques such as bagging and boosting to combat overfitting. For instance, Random Forest combines multiple decision trees to enhance accuracy and mitigate overfitting by averaging predictions. Boosting algorithms like AdaBoost, Gradient Boosting, and XGBoost sequentially improve model performance, reducing both bias and variance.
+2) In decision trees, pruning can remove the branches that have little power in classifying instances, which can reduce overfitting. It can reduce the size and complexity of the tree, and improve its generalization and interpretation. Pruning can be applied either before or after the tree is fully grown, using different methods and criteria.
+3) Hyperparameter Tuning like grid search, random search, or Bayesian optimization to find the optimal set of hyperparameters that minimize overfitting.


Adjusting class weights is primarily a strategy to address class imbalance rather than overfitting. While it helps the model focus more on minority classes, it doesn't directly prevent overfitting.

eduard-balamatiuc · 2025-06-11T17:06:15Z

ai-reviewer have a look

github-actions · 2025-06-11T17:06:50Z

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github-actions · 2025-06-11T17:07:32Z

🤖 AI Article Review

👍 Acceptable article with room for enhancement.

Overall Score: 6.5/10

📄 Files Reviewed: 2
⏰ Review Completed: 2025-06-11T17:07:32Z

Summary

Score: 6.5/10
Reviewed 2 files. Individual scores: README.MD: 5/10, article.md: 8/10

💡 Key Suggestions

article-under-overfitting/README.MD: Provide detailed explanations and examples for each technique mentioned, such as regularization and cross-validation.
article-under-overfitting/README.MD: Include a proper code example that demonstrates the application of these techniques in a classification model.
article-under-overfitting/README.MD: Expand on the 'Further Exploration' section with specific project ideas or exercises to engage readers.
article-under-overfitting/article.md: Streamline the sections on underfitting to reduce redundancy and improve readability.
article-under-overfitting/article.md: Include references to recent research or advancements in the field to support the claims made.
article-under-overfitting/article.md: Provide practical examples or case studies to illustrate the application of the discussed techniques.

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add under/over-fitting article#19

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		## Underfitting

		Conversely, underfitting in classification models arises when the model fails to capture the underlying patterns present in the data adequately. This deficiency often stems from the use of overly simplistic models or inadequate model training. For instance, employing linear classifiers in scenarios with nonlinear decision boundaries may result in underfitting, leading to suboptimal classification performance. Furthermore, underfitting can be exacerbated by factors such as feature scaling, imbalanced class distributions, or insufficient model complexity. In such cases, the model may struggle to discern meaningful patterns, resulting in poor predictive accuracy across both training and validation datasets. Addressing underfitting requires careful consideration of model selection, feature engineering, and optimization techniques to ensure that the model can effectively capture the complexity of the classification task while avoiding unnecessary bias or simplification.Conversely, underfitting in classification models arises when the model fails to capture the underlying patterns present in the data adequately. This deficiency often stems from the use of overly simplistic models or inadequate model training. For instance, employing linear classifiers in scenarios with nonlinear decision boundaries may result in underfitting, leading to suboptimal classification performance. Furthermore, underfitting can be exacerbated by factors such as feature scaling, imbalanced class distributions, or insufficient model complexity. In such cases, the model may struggle to discern meaningful patterns, resulting in poor predictive accuracy across both training and validation datasets. Addressing underfitting requires careful consideration of model selection, feature engineering, and optimization techniques to ensure that the model can effectively capture the complexity of the classification task while avoiding unnecessary bias or simplification.

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