SeraML — Machine Learning

All models are sklearn-compatible. Swap the import, keep the code.

📈 Linear

• LinearRegression — Ordinary Least Squares linear regression
• Ridge — Ridge regression
• Lasso — Lasso regression
• ElasticNet — ElasticNet
• LogisticRegression — Logistic regression
• RidgeClassifier — Ridge classifier
• SgdClassifier — SGDClassifier
• SgdRegressor — SGDRegressor

🌲 Tree-Based

• DecisionTreeClassifier — Decision tree classifier
• DecisionTreeRegressor — Decision tree regressor
• RandomForestClassifier — Random Forest classifier
• RandomForestRegressor — Random Forest regressor
• GradientBoostingClassifier — Gradient Boosting classifier
• GradientBoostingRegressor — Gradient Boosting regressor
• AdaboostClassifier — AdaBoost classifier
• AdaboostRegressor — AdaBoost regressor

🔍 Neighbors

• KnnClassifier — K-Nearest Neighbors classifier
• KnnRegressor — K-Nearest Neighbors regressor

📊 Naive Bayes

• GaussianNb — Gaussian Naive Bayes

⚡ SVM

• LinearSvc — LinearSVC
• LinearSvr — LinearSVR

🔬 Decomposition

• Pca — PCA

🔮 Clustering

• DbscanFitPredict — DBSCAN
• KmeansFitPredict — K-Means

🔧 Preprocessing

• StandardScaler — StandardScaler

🚨 Anomaly Detection

• IsolationForest — IsolationForest

⚙️ Model Selection

• PermutationImportance — Permutation importance
• KfoldSplit — K-Fold split
• GridSearchCv — Grid search with cross-validation

📦 Registry

• SaveModel — Save model to the in-memory registry with name, version, and metadata.

📏 Metrics

• MetricScore — Compute a named metric score (accuracy, r2, f1, etc.) from predictions.