5 Lightweight ML Frameworks You Should Know in 2026

 

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.com 
Output:
[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.com 

Output:
Predictions: [0.59171517 0.79370218 0.41264801 0.71209377 0.61403022 0.11052331
 0.18246353 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.com 

Output:
Predictions: [0.59171517 0.79370218 0.41264801 0.71209377 0.61403022 0.11052331
 0.18246353 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.com 

Output:
Checking whether there is an H2O instance running at http://localhost:54321.....