Machine Learning Libraries: A Guide With Practical Snippets

from sklearn.datasets import load_iris

from sklearn.model_selection import train_test_split

from sklearn.ensemble import RandomForestClassifier

iris = load_iris()

X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.3)

model = RandomForestClassifier()

model.fit(X_train, y_train)

predictions = model.predict(X_test)

print("Predictions:", predictions)

import tensorflow as tf

from tensorflow.keras.models import Sequential

from tensorflow.keras.layers import Dense

model = Sequential([

Dense(32, activation='relu', input_shape=(10,)),

Dense(1, activation='sigmoid')

])

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

model.fit(X_train, y_train, epochs=10)

import torch

import torch.nn as nn

import torch.optim as optim

class Net(nn.Module):

def __init__(self):

super(Net, self).__init__()

self.fc = nn.Linear(10, 1)


def forward(self, x):

return torch.sigmoid(self.fc(x))

model = Net()

criterion = nn.BCELoss()

optimizer = optim.Adam(model.parameters(), lr=0.01)

for epoch in range(10):

inputs = torch.randn(16, 10)

labels = torch.randint(0, 2, (16, 1)).float()

optimizer.zero_grad()

output = model(inputs)

loss = criterion(output, labels)

loss.backward()

optimizer.step()

import xgboost as xgb

from sklearn.datasets import load_breast_cancer

from sklearn.model_selection import train_test_split

data = load_breast_cancer()

X_train, X_test, y_train, y_test = train_test_split(data.data, data.target)

model = xgb.XGBClassifier()

model.fit(X_train, y_train)

predictions = model.predict(X_test)

print(predictions)

import lightgbm as lgb


train_data = lgb.Dataset(X_train, label=y_train)

test_data = lgb.Dataset(X_test, label=y_test)



params = {

'objective': 'binary',

'metric': 'binary_logloss',

'verbose': -1

}


model = lgb.train(params, train_data, valid_sets=[test_data], num_boost_round=100)