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Data Science

Classification Algorithms in Machine Learning

Data Science 266 views Nov 14, 2025 1 min read

Introduction

Classification is used to predict categories such as spam/not-spam, fraud detection, or disease detection.

1. Logistic Regression


from sklearn.linear_model import LogisticRegression
model = LogisticRegression()

2. Decision Tree Classifier


from sklearn.tree import DecisionTreeClassifier
model = DecisionTreeClassifier()

3. K-Nearest Neighbors (KNN)


from sklearn.neighbors import KNeighborsClassifier
model = KNeighborsClassifier(n_neighbors=5)

4. Support Vector Machine (SVM)


from sklearn.svm import SVC
model = SVC()

5. Naive Bayes


from sklearn.naive_bayes import GaussianNB
model = GaussianNB()

6. Random Forest Classifier


from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier()

7. Evaluation Metrics

  • Accuracy Score
  • Precision
  • Recall
  • F1 Score
  • Confusion Matrix
  • ROC Curve, AUC

Conclusion

Classification algorithms are powerful for predicting class labels. Selecting the right algorithm depends on dataset size, balance, and feature complexity.

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