Hyperparameter Space for Different Algorithms

# Define the hyperparameter space for linear regression
param_dist = {
    'fit_intercept': [True, False],
    'normalize': [True, False],
    'copy_X': [True, False]
}

# Define the hyperparameter space for lasso regression
param_dist = {
    'alpha': [0.1, 1.0, 10.0],
    'fit_intercept': [True, False],
    'normalize': [True, False],
    'copy_X': [True, False]
}

# Define the hyperparameter space for ridge regression
param_dist = {
    'alpha': [0.1, 1.0, 10.0],
    'fit_intercept': [True, False],
    'normalize': [True, False],
    'solver': ['auto', 'svd', 'cholesky', 'lsqr', 'sparse_cg', 'sag', 'saga']
}

# Define the hyperparameter space for logistic regression
param_dist = {
    'penalty': ['l1', 'l2'],
    'C': [0.1, 1.0, 10.0],
    'fit_intercept': [True, False],
    'solver': ['liblinear', 'saga'],
    'max_iter': [100, 200, 500]
}

# Define the hyperparameter space for KNN
param_dist = {
    'n_neighbors': [3, 5, 7, 9],
    'weights': ['uniform', 'distance'],
    'algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute'],
    'p': [1, 2]
}

# Define the hyperparameter space for SVM
param_dist = {
    'C': [0.1, 1.0, 10.0],
    'kernel': ['linear', 'poly', 'rbf', 'sigmoid'],
    'gamma': ['scale', 'auto'],
    'degree': [2, 3, 4]
}

# Define the hyperparameter space for Naive Bayes
param_dist = {
    'var_smoothing': [1e-9, 1e-8, 1e-7, 1e-6]
}

# Define the hyperparameter space for Decision Tree
param_dist = {
    'criterion': ['gini', 'entropy'],
    'max_depth': [None, 5, 10, 15],
    'min_samples_split': [2, 5, 10],
    'min_samples_leaf': [1, 2, 4],
    'max_features': ['auto', 'sqrt', 'log2', None]
}

# Define the hyperparameter space for Random forest
param_dist = {
    'n_estimators': [100, 200, 300],
    'criterion': ['gini', 'entropy'],
    'max_depth': [None, 5, 10],
    'min_samples_split': [2, 5, 10],
    'min_samples_leaf': [1, 2, 4],
    'max_features': ['auto', 'sqrt', 'log2']
}

# Define the hyperparameter space for XGBOOST
param_dist = {
    'learning_rate': [0.1, 0.01, 0.001],
    'max_depth': [3, 5, 7],
    'n_estimators': [100, 200, 300],
    'subsample': [0.6, 0.8, 1.0],
    'colsample_bytree': [0.6, 0.8, 1.0],
    'gamma': [0, 1, 5]
}

# Define the hyperparameter space for K Means Clustering
param_dist = {
    'n_clusters': [2, 3, 4, 5],
    'init': ['k-means++', 'random'],
    'n_init': [10, 20, 30],
    'max_iter': [100, 200, 300]
}

# Define the hyperparameter space for DBScan Clustering
param_dist = {
    'eps': [0.1, 0.3, 0.5],
    'min_samples': [2, 5, 10],
    'metric': ['euclidean', 'manhattan', 'chebyshev']
}

# Define the hyperparameter space for ANN
param_dist = {
    'hidden_layers': [1, 2, 3],
    'units': [16, 32, 64],
    'activation': ['relu', 'sigmoid'],
    'optimizer': ['adam', 'sgd'],
    'epochs': [10, 20, 30],
    'batch_size': [8, 16, 32]
}

# Define the hyperparameter space for CNN
param_dist = {
    'filters': [16, 32, 64],
    'kernel_size': [(3, 3), (5, 5)],
    'pool_size': [(2, 2), (3, 3)],
    'hidden_units': [64, 128, 256],
    'optimizer': ['adam', 'sgd'],
    'epochs': [10, 20, 30],
    'batch_size': [8, 16, 32]
}