CatBoost, a popular machine learning library, offers various functionalities to enhance model training. One such feature is the ability to assign weights to training data, allowing you to give more importance to certain instances in the dataset.

In this blog post, we will discuss how to set weights for training data in CatBoost using Python.

Why set weights for training data?

Setting weights for training data is beneficial in scenarios where some instances are more important or informative than others. By assigning higher weights to certain instances, you can emphasize their influence during model training.

For example, in a binary classification problem, if the instances of one class are rare but crucial to identify correctly, you can assign higher weights to those instances. This ensures that the model focuses more on learning patterns from the informative instances.

Now let’s see how to implement this in CatBoost.

Setting weights for training data in CatBoost

To set weights for training data in CatBoost, you need to follow these steps:

1. Create a CatBoost Pool object from your training data.
2. Define weights for each instance in the dataset.
3. Assign the weights to the Pool object.

Let’s look at an example:

import catboost as cb
import numpy as np

# Define your training data and labels
X_train = np.array([[1, 2], [3, 4], [5, 6], [7, 8]])
y_train = np.array([0, 1, 0, 1])

# Create a CatBoost Pool object
train_pool = cb.Pool(data=X_train, label=y_train)

# Define weights for each instance
weights = [1, 2, 1, 3]

# Assign the weights to the Pool object
train_pool.set_weight(weights)

# Specify the parameters for your CatBoost model
params = {
    'iterations': 100,
    'learning_rate': 0.1,
    'depth': 6
}

# Train the CatBoost model with weighted data
model = cb.CatBoostClassifier(**params)
model.fit(train_pool)

In the code snippet above, we first create a CatBoost Pool object train_pool from our training data X_train and labels y_train. Next, we assign weights to each instance using the set_weight method of the Pool object. Finally, we train a CatBoost classifier model with the weighted data.

Conclusion

In this blog post, we discussed how to set weights for training data in CatBoost using Python. Assigning weights to specific instances allows you to emphasize their importance during model training. By following the steps mentioned above, you can effectively utilize the weight functionality in CatBoost and improve the performance of your machine learning models.

Remember, weighting training instances should be done thoughtfully, considering the specific characteristics of your dataset.