Entrenamiento y evaluación de regresión logística multiclase

How to train a multiclass logistic regression model?

Logistic regression is one of the most widely used techniques in data classification. It allows to efficiently categorize a set of data into several classes, facilitating the understanding of the behavior of the data. In this sense, we are going to explain how to train a multiclass logistic regression model using LogisticRegression from the Scikit-learn Python library by using parameters such as solver, multi_class, and C, as well as iterating over different combinations to obtain the best possible model.

What steps are followed to create the model?

To begin, it is necessary to define the variables and parameters that will be used in training the model. The steps are:

1. Define the model: We use LogisticRegression by specifying key parameters. An example is the random state to ensure repeatable results.

   from sklearn.linear_model import LogisticRegression
   logistic_regression_model = LogisticRegression( random_state=42,  solver='saga',  multi_class='multinomial', n_jobs=-1,  C=1.0)

2. Create a function: To dynamically manage the parameters, we can create a function that accepts the parameters C, solver and multi_class.

   def logistic_model(C, solver, multi_class): return LogisticRegression(  C=C,  solver=solver,  multi_class=multi_class, n_jobs=-1, random_state=42 )

3. Train the model: Once defined, train the model with the training data and make predictions.

   model = logistic_model(1, 'saga', 'multinomial')model.fit(X_train, y_train)predictions = model.predict(X_test)

4. Evaluate results: It is crucial to evaluate the accuracy of the model using metrics such as the confusion matrix and the accuracy score.

   from sklearn.metrics import confusion_matrix, accuracy_score
   cm = confusion_matrix(y_test, predictions)accuracy = accuracy_score(y_test, predictions)print('Confusion Matrix:\n', cm)print('Accuracy:', accuracy)

How to improve the model?

A good practice to optimize the model is to try different combinations of solver and multi_class and see which one gives better results.

1. Iterate over combinations: Use loops to iterate through possible values for multi_class and solver.

   multiclass_options = ['ovr', 'multinomial']solver_list = ['newton-cg', 'saga', 'liblinear', 'sag']
   best_score = 0best_params = {}
   for mc in multiclass_options: for solver in solver_list: try: model = logistic_model(1, solver, mc) model.fit(X_train, y_train) predictions = model.predict(X_test)
    accuracy = accuracy_score(y_test, predictions) if accuracy > best_score: best_score = accuracy best_params = {'solver': solver, 'multi_class':  mc}
    except Exception as e: # Handle exceptions for incompatible configurations continue
   print('Best Score:', best_score)print('Best Params:', best_params)

2. Visualize the results: Use graphs to analyze the results obtained to select the most appropriate model.

   import matplotlib.pyplot as pltimport seaborn as sns
   sns.barplot(x=best_params.keys(),  y=best_params.values())plt.title('Scores with different solvers and multi_class options')plt.xticks(rotation=90)plt.show()

This process may seem exhaustive, but it is crucial to understand the performance of each configuration and select the best model for multi-classification.

Why is hyperparameter tuning important?

Adjusting the hyperparameters allows you to:

• Obtain a more accurate model: by increasing the correct classification rate.
• Improve computational efficiency: Adapting resources to the problem.
• Increase the robustness of the model: Against noise and outliers.

The key to success in multiclass logistic regression lies in performing a thorough analysis of the results and adjusting the parameters appropriately. In this way, we can guarantee the implementation of a model that not only fulfills the classification task, but does so with a high degree of accuracy. Keep exploring and improving your models to achieve better performance in your machine learning projects!