How to Integrate Catboost Classification Metrics?
Interpreting Catboost classification metrics requires a deep understanding of the problem domain and the goals of the project. Here are some general guidelines:
- High accuracy and F1-score indicate that the model is performing well overall.
- High precision and low recall suggest that the model is conservative in its predictions, missing some true positives.
- High recall and low precision indicate that the model is aggressive in its predictions, resulting in more false positives.
- High AUC-ROC indicates that the model is good at distinguishing between positive and negative classes.
- Low logloss and cross-entropy indicate that the model is confident in its predictions.
Lets take an example to point out an instance of catboost classification metrics on Iris Dataset using demographics information.
To implement Catboost classification metrics in your project, follow these steps:
- Train the boost model on your dataset to get the model.
- Then we need to predict the target variable using the trained model.
- Evaluate the metric to get the output for accuracy, precision, recall, F1-score, and ROC-AUC respectively through Catboost accuracy, precision, recall, F1_score, auc_roc.
Implement Catboost Algorithm
from sklearn.datasets import load_iris
from catboost import CatBoostClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, confusion_matrix, cohen_kappa_score
iris = load_iris()
X = iris.data
y = iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Train CatBoost model
model = CatBoostClassifier(iterations=50, learning_rate=0.1, eval_metric='AUC') # Adjust hyperparameters as needed
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
y_pred_proba = model.predict_proba(X_test)
Calculate Catboost Classification Metrics
# Calculate evaluation metrics
metrics = {}
metrics['Accuracy'] = accuracy_score(y_test, y_pred)
metrics['Precision'] = precision_score(y_test, y_pred, average='macro') # Macro averaging for imbalanced data
metrics['Recall'] = recall_score(y_test, y_pred, average='macro') # Macro averaging for imbalanced data
metrics['F1 Score'] = f1_score(y_test, y_pred, average='macro') # Macro averaging for imbalanced data
metrics['Kappa'] = cohen_kappa_score(y_test, y_pred)
# Display metrics
print('Metrics:')
for metric, value in metrics.items():
print(f'{metric}: {value}')
# Confusion Matrix
conf_matrix = confusion_matrix(y_test, y_pred)
print('Confusion Matrix:')
print(np.array2string(conf_matrix, suppress_small=True))
Output:
Metrics:
Accuracy: 1.0
Precision: 1.0
Recall: 1.0
F1 Score: 1.0
Kappa: 1.0
Confusion Matrix:
[[10 0 0]
[ 0 9 0]
[ 0 0 11]]
Visualize AUC Graph
# Get unique class labels
class_labels = np.unique(y)
# Plot ROC curves for each class
plt.figure(figsize=(8, 6))
for i, label in enumerate(class_labels):
fpr, tpr, _ = roc_curve(y_test == label, y_pred_proba[:, i])
roc_auc = roc_auc_score(y_test == label, y_pred_proba[:, i])
plt.plot(fpr, tpr, label=f'Class {label} (AUC-ROC={roc_auc:.4f})')
plt.legend()
# Plot ROC curve for all classes (optional)
# all_fpr, all_tpr, _ = roc_curve(y_test, y_pred_proba[:, 0], multi_class='ovr')
# plt.plot(all_fpr, all_tpr, label='Multi-class ROC (ovr)')
plt.xlabel('False Positive Rate (FPR)')
plt.ylabel('True Positive Rate (TPR)')
plt.title('ROC Curves for Iris Flower Classification (CatBoost)')
plt.grid(True)
plt.xlim(0, 1)
plt.ylim(0, 1.05)
plt.show()
Output:
AUC-ROC graph
Catboost Classification Metrics
When it comes to machine learning, classification is a fundamental task that involves predicting a categorical label or class based on a set of input features. One of the most popular and efficient algorithms for classification is Catboost, a gradient boosting library developed by Yandex.
Catboost is known for its speed, accuracy, and ease of use, making it a favorite among data scientists and machine learning practitioners. However, to fully leverage the power of Catboost, it’s essential to understand the various metrics used to evaluate the performance of classification models.
In this article, we’ll delve into the world of Catboost classification metrics, exploring what they are, how they work, and how to interpret them.
Table of Content
- What are Classification Metrics?
- Common Catboost Classification Metrics
- How to Integrate Catboost Classification Metrics?
- Choosing the Right Metric
- Best Practices for Using Catboost Classification Metrics
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