Common Machine Learning Algorithms: The Practical Guide

Key Algorithms:

• Decision Trees: Simple interpretable models
• Random Forest: Ensemble of decorrelated trees
• XGBoost: Gradient-boosted trees
• LightGBM: Histogram-based boosting

When to Use:

• Tabular data with mixed feature types
• Need for feature importance scores
• Problems requiring non-linear decision boundaries

Python Implementation:

from sklearn.ensemble import RandomForestClassifier
from xgboost import XGBClassifier

# Random Forest
rf = RandomForestClassifier(n_estimators=100)
rf.fit(X_train, y_train)

# XGBoost
xgb = XGBClassifier(n_estimators=100, learning_rate=0.1)
xgb.fit(X_train, y_train)
        

Key Algorithms:

Algorithm	Task	Key Feature
Linear Regression	Regression	Minimizes MSE
Logistic Regression	Classification	Sigmoid function
Ridge/Lasso	Regularized	Prevents overfitting

Strengths:

• Interpretable coefficients
• Fast training/prediction
• Works well with high-dimensional data

Python Example:

from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report

# With L2 regularization
model = LogisticRegression(penalty='l2', C=1.0)
model.fit(X_train, y_train)

# Evaluate
print(classification_report(y_test, model.predict(X_test)))
        

Key Architectures:

Implementation:

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

# Simple MLP
model = Sequential([
    Dense(64, activation='relu', input_shape=(10,)),
    Dense(32, activation='relu'),
    Dense(1, activation='sigmoid')
])

model.compile(optimizer='adam',
              loss='binary_crossentropy',
              metrics=['accuracy'])
model.fit(X_train, y_train, epochs=10)
        

When to Choose:

For complex patterns in unstructured data (images, text) or when other algorithms plateau in performance

Key Techniques:

• k-Means: Centroid-based clustering
• DBSCAN: Density-based clustering
• PCA: Linear dimensionality reduction
• t-SNE: Nonlinear visualization

Implementation Example:

from sklearn.cluster import KMeans
from sklearn.decomposition import PCA

# Dimensionality reduction
pca = PCA(n_components=2)
X_pca = pca.fit_transform(X)

# Clustering
kmeans = KMeans(n_clusters=3)
clusters = kmeans.fit_predict(X_pca)
 

Use Cases:

Customer segmentation, anomaly detection, data visualization, and feature engineering