{"id":15132678,"url":"https://github.com/harmanveer-2546/guide-to-regularization","last_synced_at":"2025-04-05T21:44:58.286Z","repository":{"id":255996600,"uuid":"854070059","full_name":"harmanveer-2546/Guide-to-Regularization","owner":"harmanveer-2546","description":"Regularization is a crucial technique in machine learning that helps to prevent overfitting. Overfitting occurs when a model becomes too complex and learns the training data so well that it fails to generalize to new, unseen data. ","archived":false,"fork":false,"pushed_at":"2024-09-08T10:37:44.000Z","size":900,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-05T21:44:51.212Z","etag":null,"topics":["generalization","inline","l1","l2","matplotlib","numpy","overfitting","overfitting-prevention","overfitting-reduced","pandas","regularization","regularization-methods","regularization-techniques","regularization-to-avoid-overfitting","seaborn"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/harmanveer-2546.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-09-08T10:29:37.000Z","updated_at":"2024-09-08T10:37:47.000Z","dependencies_parsed_at":null,"dependency_job_id":"dbc3225c-aaae-4ef5-800b-0bf5ce057e5a","html_url":"https://github.com/harmanveer-2546/Guide-to-Regularization","commit_stats":{"total_commits":2,"total_committers":1,"mean_commits":2.0,"dds":0.0,"last_synced_commit":"b0f4499390ec4a55c8c658d6d3862d3296b34f40"},"previous_names":["harmanveer-2546/guide-to-regularization"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/harmanveer-2546%2FGuide-to-Regularization","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/harmanveer-2546%2FGuide-to-Regularization/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/harmanveer-2546%2FGuide-to-Regularization/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/harmanveer-2546%2FGuide-to-Regularization/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/harmanveer-2546","download_url":"https://codeload.github.com/harmanveer-2546/Guide-to-Regularization/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247406068,"owners_count":20933802,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["generalization","inline","l1","l2","matplotlib","numpy","overfitting","overfitting-prevention","overfitting-reduced","pandas","regularization","regularization-methods","regularization-techniques","regularization-to-avoid-overfitting","seaborn"],"created_at":"2024-09-26T04:22:16.231Z","updated_at":"2025-04-05T21:44:58.277Z","avatar_url":"https://github.com/harmanveer-2546.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Preventing Overfitting: A Guide to Regularization\n\nRegularization is a crucial technique in machine learning that helps to prevent overfitting. Overfitting occurs \nwhen a model becomes too complex and learns the training data so well that it fails to generalize to new, unseen \ndata. This can lead to poor performance on real-world applications.  \n\nBy introducing a penalty term to the loss function, regularization discourages models from becoming overly complex.\nThis penalty term is calculated based on the magnitude of the model's parameters.  \n\n### Common Regularization Techniques:\n\n* L1 Regularization (Lasso): This technique encourages sparsity, meaning many model parameters are driven to zero. This can be useful for feature selection,\n     as it can help identify the most important features.  \n* L2 Regularization (Ridge): L2 regularization prevents individual parameters from becoming too large, which can help to reduce the variance of the model.  \n* Elastic Net: This is a combination of L1 and L2 regularization, which can be useful when both feature selection and reducing variance are important.\n\n### When to Use Regularization:\n\n* Limited Training Data: When you have limited training data, regularization can help prevent overfitting by preventing the model from memorizing the training set.\n* High-Dimensional Data: With many features, regularization can help to prevent overfitting by reducing the complexity of the model.\n* Preventing Overfitting: Regularization is a general technique for preventing overfitting in various machine learning models.\n\n### Key Benefits of Regularization:\n\n* Improved Generalization: Regularization helps models generalize unseen data better.  \n* Reduced Overfitting: It prevents models from becoming too complex and memorizing the training data.  \n* Feature Selection: L1 regularization can be used for feature selection.  \n* Enhanced Model Stability: Regularization can make models more stable and less sensitive to small changes in the data.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fharmanveer-2546%2Fguide-to-regularization","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fharmanveer-2546%2Fguide-to-regularization","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fharmanveer-2546%2Fguide-to-regularization/lists"}