multiclasshead - Python

The multiclasshead package is a Python library that provides a multi-class classifier for machine learning tasks. It is built on top of scikit-learn and provides a simple interface for training and predicting with multi-class classifiers.

Features

• Provides a simple and consistent interface for training and predicting with multi-class classifiers.
• Supports a variety of classifiers, including logistic regression, decision trees, random forests, and gradient boosting.
• Provides metrics for evaluating classifier performance, including accuracy, precision, and recall.
• Supports the creation of custom classifiers by allowing users to supply their own classifier object.

Installation

To install multiclasshead, simply run the following command:

!pip install multiclasshead

Usage

Training a Classifier

To train a multi-class classifier, create an instance of the MultiClassClassifier class and pass in a classifier object and the training data. For example, to train a logistic regression classifier on a dataset containing three classes, you would do the following:

from multiclasshead import MultiClassClassifier
from sklearn.linear_model import LogisticRegression

X_train = ...
y_train = ...

clf = LogisticRegression()

multi_clf = MultiClassClassifier(clf)
multi_clf.fit(X_train, y_train)

Evaluating Classifier Performance

To evaluate the performance of a classifier, use the score method of the MultiClassClassifier object. This method will return a dictionary containing the accuracy, precision, recall, and F1-score for the classifier.

X_test = ...
y_test = ...

score = multi_clf.score(X_test, y_test)
print(score)

Predicting Class Labels

To predict the class labels for new data, use the predict method of the MultiClassClassifier object.

X_new = ...

y_pred = multi_clf.predict(X_new)
print(y_pred)

Custom Classifiers

To create a custom classifier, simply create a new object that implements the scikit-learn estimator interface. For example, to create a custom decision tree classifier, you would do the following:

from sklearn.tree import DecisionTreeClassifier

class MyDecisionTreeClassifier(DecisionTreeClassifier):

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

    def fit(self, X, y):
        # Custom fit logic goes here
        ...

    def predict(self, X):
        # Custom prediction logic goes here
        ...

clf = MyDecisionTreeClassifier()

multi_clf = MultiClassClassifier(clf)
multi_clf.fit(X_train, y_train)

Conclusion

multiclasshead is a powerful library for multi-class classification tasks in Python. With its simple and consistent interface, you can quickly train and evaluate multi-class classifiers using a variety of models. By supporting custom classifiers, you can also extend its functionality to meet the needs of your specific project.