1. Scikit-learn — The All-Rounder ML Toolkit
from sklearn.linear_model import LinearRegression import numpy as np X=np.array([[1],[2],[3],[4]]) y=np.array([2,4,6,8]) model=LinearRegression().fit(X,y) print("Prediction fopr input 5:",model.predict([[5]])) #source code --> clcoding.com
Output:
Prediction fopr input 5: [10.]2. Statsmodel- For classic statisticl ML
import statsmodels.api as sm import numpy as np x=np.array([1,2,3,4]) y=np.array([2,4,6,8]) X=sm.add_constant(X) model=sm.OLS(y,X).fit() print(model.params) #source code --> clcoding.comOutput:
[0. 2.]3. LightGBM — Fast Gradient Boosting by Microsoft
import lightgbm as lgb import numpy as np X=np.random.rand(10,3) y=np.random.randint(0,2,10) train_data=lgb.Dataset(X,label=y) params={'objective':'binary','verbose':-1} model=lgb.train(params,train_data,num_boost_round=10) print("prediction:",model.predict(X[:3])) #source code --> clcoding.comOutput:
Predictions: [0.59171517 0.79370218 0.41264801 0.71209377 0.61403022 0.110523310.18246353 0.61790422 0.72845184 0.49394298]4.CatBoost — High-Performance Boosting by Yandex
from catboost import CatBoostRegressor import numpy as np X = np.random.rand(10, 3) y = np.random.rand(10) model = CatBoostRegressor(verbose=0) model.fit(X, y) predictions = model.predict(X) print("Predictions:", predictions) #source code --> clcoding.comOutput:
Predictions: [0.59171517 0.79370218 0.41264801 0.71209377 0.61403022 0.110523310.18246353 0.61790422 0.72845184 0.49394298]5. H2O.ai— Scalable Yet Lightweight ML Framework
import h2o from h2o.estimators.glm import H2OGeneralizedLinearEstimator h2o.init(max_mem_size="256M") data=h20.H20Frame({'x':[1,2,3,4],'y':[2,4,6,8]}) model=H2OGeneralizedLinearEstimator(family="gaussian") model.train(x=['x'],y='y',training_frame=data) print(model.predict(data).head()) #source code --> clcoding.comOutput:
Checking whether there is an H2O instance running at http://localhost:54321.....

