Classification Algorithms in Machine Learning

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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.