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import streamlit as st
from sklearn.linear_model import LinearRegression, Ridge, Lasso, ElasticNet
from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor
@st.cache_data
def train_selected_regression_model(X_train, Y_train, model_type, model_params=None):
"""
Trains a regression model based on the specified model type and parameters.
Parameters:
- X_train (array-like): The training input samples.
- Y_train (array-like): The target values (real numbers).
- model_type (int): An integer representing the type of regression model to train.
1 for Linear Regression, 2 for Ridge Regression, 3 for Lasso Regression,
4 for Random Forest Regressor, 5 for Gradient Boosting Regressor, and 6 for ElasticNet Regression.
- model_params (dict, optional): A dictionary of model-specific parameters. Default is None.
Returns:
- The trained regression model object based on the specified model type.
"""
if model_type == 1:
return LinearRegression_train(X_train, Y_train, model_params)
elif model_type == 2:
return RidgeRegression_train(X_train, Y_train, model_params)
elif model_type == 3:
return LassoRegression_train(X_train, Y_train, model_params)
elif model_type == 4:
return RandomForestRegressor_train(X_train, Y_train, model_params)
elif model_type == 5:
return GradientBoostingRegressor_train(X_train, Y_train, model_params)
elif model_type == 6:
return ElasticNetRegressor_train(X_train, Y_train, model_params)
def LinearRegression_train(X_train, Y_train, model_params=None):
if model_params is None: model_params = {}
lr = LinearRegression(**model_params)
lr.fit(X_train, Y_train)
return lr
def RidgeRegression_train(X_train, Y_train, model_params=None):
if model_params is None: model_params = {}
ridge = Ridge(**model_params)
ridge.fit(X_train, Y_train)
return ridge
def LassoRegression_train(X_train, Y_train, model_params=None):
if model_params is None: model_params = {}
lasso = Lasso(**model_params)
lasso.fit(X_train, Y_train)
return lasso
def RandomForestRegressor_train(X_train, Y_train, model_params=None):
if model_params is None: model_params = {}
rf = RandomForestRegressor(**model_params)
rf.fit(X_train, Y_train)
return rf
def GradientBoostingRegressor_train(X_train, Y_train, model_params=None):
if model_params is None: model_params = {}
gbr = GradientBoostingRegressor(**model_params)
gbr.fit(X_train, Y_train)
return gbr
def ElasticNetRegressor_train(X_train, Y_train, model_params=None):
if model_params is None: model_params = {}
en = ElasticNet(**model_params)
en.fit(X_train, Y_train)
return en |