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| import streamlit as st | |
| from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, AdaBoostClassifier | |
| from sklearn.linear_model import LogisticRegression | |
| from sklearn.naive_bayes import GaussianNB | |
| from sklearn.svm import SVC | |
| from xgboost import XGBClassifier | |
| def train_selected_model(X_train, Y_train, model_type, model_params=None): | |
| """ | |
| Trains a specific classification model based on the provided model type and parameters. | |
| Parameters: | |
| - X_train (array-like): The training input samples. | |
| - Y_train (array-like): The target labels for classification. | |
| - model_type (int): Specifies the type of classification model to be trained. | |
| 1 for Logistic Regression, 2 for Support Vector Machine (SVM), 3 for Naive Bayes, | |
| 4 for Random Forest, 5 for AdaBoost, 6 for XGBoost, and 7 for Gradient Boosting. | |
| - model_params (dict, optional): A dictionary of parameters for the model. Defaults to None. | |
| Returns: | |
| - model: The trained model object based on the specified type. | |
| """ | |
| if model_type == 1: | |
| return LogisticRegression_train(X_train, Y_train, model_params) | |
| elif model_type == 2: | |
| return SVM_train(X_train, Y_train, model_params) | |
| elif model_type == 3: | |
| return NaiveBayes_train(X_train, Y_train, model_params) | |
| elif model_type == 4: | |
| return RandomForest_train(X_train, Y_train, model_params=model_params) | |
| elif model_type == 5: | |
| return AdaBoost_train(X_train, Y_train, model_params) | |
| elif model_type == 6: | |
| return XGBoost_train(X_train, Y_train, model_params) | |
| elif model_type == 7: | |
| return GradientBoosting_train(X_train, Y_train, model_params) | |
| def LogisticRegression_train(X_train, Y_train, model_params=None): | |
| if model_params is None: model_params = {} | |
| logreg = LogisticRegression(**model_params) | |
| logreg.fit(X_train, Y_train) | |
| return logreg | |
| def SVM_train(X_train, Y_train, model_params=None): | |
| if model_params is None: model_params = {} | |
| svm = SVC(**model_params) | |
| svm.fit(X_train, Y_train) | |
| return svm | |
| def NaiveBayes_train(X_train, Y_train, model_params=None): | |
| if model_params is None: model_params = {} | |
| nb = GaussianNB(**model_params) | |
| nb.fit(X_train, Y_train) | |
| return nb | |
| def RandomForest_train(X_train, Y_train, n_estimators=100, random_state=None, model_params=None): | |
| if model_params is None: model_params = {} | |
| rf_params = {'n_estimators': n_estimators, 'random_state': random_state} | |
| rf_params.update(model_params) | |
| rf = RandomForestClassifier(**rf_params) | |
| rf.fit(X_train, Y_train) | |
| return rf | |
| def AdaBoost_train(X_train, Y_train, model_params=None): | |
| if model_params is None: model_params = {} | |
| ab = AdaBoostClassifier(**model_params) | |
| ab.fit(X_train, Y_train) | |
| return ab | |
| def XGBoost_train(X_train, Y_train, model_params=None): | |
| if model_params is None: model_params = {} | |
| xgb = XGBClassifier(**model_params) | |
| xgb.fit(X_train, Y_train) | |
| return xgb | |
| def GradientBoosting_train(X_train, Y_train, model_params=None): | |
| if model_params is None: model_params = {} | |
| gb = GradientBoostingClassifier(**model_params) | |
| gb.fit(X_train, Y_train) | |
| return gb |