The code utilizes scikit-learn to build and evaluate a Decision Tree Classifier for car classification, demonstrating training and testing set accuracy, visualizing the decision tree, and analyzing model performance metrics.
Decision Tree:
- A Decision Tree is a tree-like model of decisions and their possible consequences. In this case, it's used for classification.
- The tree is built using the features specified in features and the target variable type.
Entropy:
- Entropy is a measure of impurity in a set of data.
- The tree is split at each node based on the feature that minimizes entropy.
This code appears to be an implementation of a Decision Tree classifier using the scikit-learn library in Python. Let's break down the code and provide explanations along with some theory and result analysis.
Code Description:
1. Importing Libraries: The necessary libraries are imported, including Pandas for data manipulation, Matplotlib and Seaborn for visualization, and scikit-learn for machine learning.
2. Loading Dataset: The code loads a dataset named cars_clus.csv using Pandas.
3. Dataset Information: Prints information about the dataset, including data types and non-null counts.
4. Viewing Dataset: Displays the first few rows of the dataset.
5. Feature Extraction: Extracts features (X) and the target variable (y) from the dataset.
6. Building and Visualizing Decision Tree: Builds a Decision Tree classifier with entropy as the criterion and a maximum depth of 12. Visualizes the decision tree.
7. Splitting Data into Train and Test Sets: Splits the data into training and testing sets.
8. Building and Visualizing Decision Tree on Training Set: Builds a Decision Tree classifier on the training set with a maximum depth of 5. Visualizes the decision tree.
9. Model Evaluation: Calculates the accuracy of the model on the training and testing sets.
10. Testing and Evaluation: Predicts the target variable on the test set and evaluates the model using accuracy, classification report, and confusion matrix.
11. Visualization: Visualizes the confusion matrix using Seaborn.
Overview of the Code:
1.Dataset Exploration:
- The dataset contains information about various car models, including features such as horsepower, fuel capacity, price, and type.
- Data types and basic statistics are explored using Pandas.
2.Feature Extraction:
- Relevant features (horsepow, fuel_cap, price, etc.) are selected for model training.
3.Decision Tree Model Building:
- A Decision Tree classifier is constructed with the criterion of entropy and a specified maximum depth.
- The initial model is trained on the entire dataset, and a decision tree visualization is generated.
4.Train-Test Split:
- The dataset is divided into training and testing sets to assess the model's generalization performance.
5.Model Training and Evaluation:
- The Decision Tree model is retrained on the training set.
- The accuracy is perfect on the training set, indicating potential overfitting.
- The model is evaluated on the test set, achieving an accuracy of approximately 83.33%.
6.Model Evaluation Metrics:
- Classification metrics such as precision, recall, and F1-score are computed and reported.
- The confusion matrix provides a detailed breakdown of true positives, true negatives, false positives, and false negatives.
7.Visualization of Results:
- A heatmap of the confusion matrix is created using Seaborn for better visualization of the model's performance.
Model Training and Evaluation:
- The model is trained on the entire dataset and then on a train-test split.
- The accuracy on the training set is perfect (1.0), but on the test set, it's 83.33%.
Confusion Matrix and Classification Report:
- The confusion matrix and classification report provide a detailed breakdown of the model's performance on the test set.
- The model performs well in predicting class 0, but struggles with precision and recall for class 1.
Visualization:
- The decision tree is visualized to provide an intuitive understanding of how the model makes decisions.
Conclusion and Analysis:
1.Model Accuracy:
- The model demonstrates good accuracy on the test set, suggesting it effectively captures patterns in the data.
- However, the perfect accuracy on the training set raises concerns about potential overfitting.
2.Class-specific Performance:
- The model performs well in predicting cars of type 0 but exhibits some challenges in predicting type 1.
- Precision, recall, and F1-score for type 1 are lower, indicating that the model struggles with this class.
3.Overfitting Consideration:
- The model achieves perfect accuracy on the training set, which may indicate overfitting, especially with a deep decision tree.
- Fine-tuning the model complexity or using techniques like pruning could address overfitting.