Kumaran1987

Below is a Python implementation of a machine learning pipeline class that supports LightGBM , XGBoost , CatBoost , and AdaBoost , using RandomizedSearchCV to find the best hyperparameters. The output includes the best hyperparameters for each model in JSON format. Prerequisites Install required libraries: pip install lightgbm xgboost catboost scikit-learn pandas numpy Code: Machine Learning Pipeline Class import json import numpy as np from sklearn.model_selection import RandomizedSearchCV, train_test_split from sklearn.ensemble import AdaBoostClassifier from xgboost import XGBClassifier from lightgbm import LGBMClassifier from catboost import CatBoostClassifier from sklearn.metrics import accuracy_score class MLBoostPipeline: def __init__(self, random_state=42, n_iter=20, cv=5): self.random_state = random_state self.n_iter = n_iter self.cv = cv self.models = { "LightGBM": LGBMClassifier(random_state=self.r...

Below is a Python implementation of a machine learning pipeline class that supports LightGBM , XGBoost , CatBoost , and AdaBoost , using RandomizedSearchCV to find the best hyperparameters. The output includes the best hyperparameters for each model in JSON format. Prerequisites Install required libraries: pip install lightgbm xgboost catboost scikit-learn pandas numpy Code: Machine Learning Pipeline Class import json import numpy as np from sklearn.model_selection import RandomizedSearchCV, train_test_split from sklearn.ensemble import AdaBoostClassifier from xgboost import XGBClassifier from lightgbm import LGBMClassifier from catboost import CatBoostClassifier from sklearn.metrics import accuracy_score class MLBoostPipeline: def __init__(self, random_state=42, n_iter=20, cv=5): self.random_state = random_state self.n_iter = n_iter self.cv = cv self.models = { "LightGBM": LGBMClassifier(random_state=self.r...