Prompt para clasificador MLP en dataset de vinos

Construye un clasificador MLP en sklearn usando el dataset de vinos (wine) de sklearn, divide los datos en conjuntos de entrenamiento (80%) y prueba (20%), escala los datos apropiadamente, calcula la precisión (accuracy), imprime el reporte de clasificación completo en el conjunto de prueba mostrando las métricas para las 3 clases de cultivares, y grafica la matriz de confusión como un heatmap usando seaborn.

Cargar y dividir los datos

Subtask:

Cargar el conjunto de datos de vinos de sklearn y dividirlo en conjuntos de entrenamiento (80%) y prueba (20%).

Reasoning: Import the necessary functions from sklearn and load and split the wine dataset into training and testing sets.

from sklearn.datasets import load_wine
from sklearn.model_selection import train_test_split

wine_data = load_wine()
X = wine_data.data
y = wine_data.target

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)
X_train.shape, X_test.shape, y_train.shape, y_test.shape

((142, 13), (36, 13), (142,), (36,))

Escalar los datos

Subtask:

Escalar las características usando un método apropiado (por ejemplo, StandardScaler).

Reasoning: Scale the features of the training and testing data using StandardScaler as instructed.

from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

Entrenar el modelo mlp

Subtask:

Inicializar y entrenar un clasificador MLP en los datos de entrenamiento escalados.

Reasoning: Import the MLPClassifier, instantiate it, and train the model on the scaled training data.

from sklearn.neural_network import MLPClassifier

mlp = MLPClassifier(random_state=42, hidden_layer_sizes=(100,), max_iter=300)
mlp.fit(X_train_scaled, y_train)

MLPClassifier(max_iter=300, random_state=42)

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Evaluar el modelo

Subtask:

Predecir en el conjunto de prueba escalado y calcular la precisión.

Reasoning: Predict on the scaled test set and calculate the accuracy.

from sklearn.metrics import accuracy_score

y_pred = mlp.predict(X_test_scaled)
accuracy = accuracy_score(y_test, y_pred)
print(f"Accuracy: {accuracy}")

Accuracy: 0.9722222222222222

Generar reporte de clasificación

Subtask:

Imprimir el reporte de clasificación completo para el conjunto de prueba mostrando las métricas para las 3 clases de cultivares.

Reasoning: Import the classification_report function, generate the classification report, and print it, ensuring all three classes are represented.

from sklearn.metrics import classification_report

print(classification_report(y_test, y_pred, target_names=wine_data.target_names))

              precision    recall  f1-score   support

     class_0       1.00      1.00      1.00        12
     class_1       0.93      1.00      0.97        14
     class_2       1.00      0.90      0.95        10

    accuracy                           0.97        36
   macro avg       0.98      0.97      0.97        36
weighted avg       0.97      0.97      0.97        36

Visualizar la matriz de confusión

Subtask:

Calcular la matriz de confusión y graficarla como un mapa de calor usando seaborn.

Reasoning: Calculate the confusion matrix and plot it as a heatmap using seaborn, labeling the axes with the target names.

from sklearn.metrics import confusion_matrix
import seaborn as sns
import matplotlib.pyplot as plt

cm = confusion_matrix(y_test, y_pred)

plt.figure(figsize=(8, 6))
sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=wine_data.target_names, yticklabels=wine_data.target_names)
plt.xlabel('Predicted Label')
plt.ylabel('True Label')
plt.title('Confusion Matrix')
plt.show()

Summary:

Data Analysis Key Findings

• The wine dataset was successfully loaded and split into training (80%) and testing (20%) sets, ensuring class distribution was maintained using stratification.
• Features were scaled using StandardScaler, fitting only on the training data to avoid data leakage.
• An MLPClassifier was initialized and trained on the scaled training data.
• The trained MLP model achieved a high accuracy on the scaled test set.
• The classification report provides detailed performance metrics (precision, recall, f1-score) for each of the three wine classes, indicating strong performance across all classes.
• The confusion matrix visualization clearly shows the counts of true positives, true negatives, false positives, and false negatives for each class, confirming the high accuracy with minimal misclassifications.

Insights or Next Steps

• The MLP model demonstrates excellent accuracy on the wine dataset, suggesting it's a very suitable model for this multi-class classification task.
• While the current results are very good, further hyperparameter tuning of the MLPClassifier could be explored, although it may not yield significant improvements given the already high performance.

Prompt para regresión

Construye un plan para un regresor MLP en sklearn usando un dataset de regresión de sklearn (diabetes), divide los datos en conjuntos de entrenamiento (80%) y prueba (20%), escala los datos apropiadamente, calcula MSE, RMSE, MAE y R² en el conjunto de prueba, y grafica un scatterplot de los valores observados vs predichos para el conjunto de prueba.

Cargar y dividir los datos

Subtask:

Cargar un conjunto de datos de regresión de sklearn y dividirlo en conjuntos de entrenamiento (80%) y prueba (20%).

Reasoning: Import the necessary functions from sklearn and load and split a regression dataset into training and testing sets.

from sklearn.datasets import load_diabetes
from sklearn.model_selection import train_test_split

diabetes_data = load_diabetes()
X = diabetes_data.data
y = diabetes_data.target

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

Escalar los datos

Subtask:

Escalar las características usando un método apropiado (por ejemplo, StandardScaler).

Reasoning: Scale the features of the training and testing data using StandardScaler as instructed.

from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

Entrenar el modelo mlp

Subtask:

Inicializar y entrenar un regresor MLP en los datos de entrenamiento escalados.

Reasoning: Import the MLPRegressor, instantiate it, and train the model on the scaled training data.

from sklearn.neural_network import MLPRegressor

mlp_regressor = MLPRegressor(random_state=42, max_iter=500)
mlp_regressor.fit(X_train_scaled, y_train)

/usr/local/lib/python3.12/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
  warnings.warn(

MLPRegressor(max_iter=500, random_state=42)

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Evaluar el modelo

Subtask:

Predecir en el conjunto de prueba escalado y calcular MSE, RMSE, MAE y R².

Reasoning: Calculate and print the evaluation metrics (MSE, RMSE, MAE, R²) for the model predictions.

import numpy as np
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score

y_pred = mlp_regressor.predict(X_test_scaled)

mse = mean_squared_error(y_test, y_pred)
rmse = np.sqrt(mse)
mae = mean_absolute_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)

print(f"Mean Squared Error (MSE): {mse}")
print(f"Root Mean Squared Error (RMSE): {rmse}")
print(f"Mean Absolute Error (MAE): {mae}")
print(f"R-squared (R²): {r2}")

Mean Squared Error (MSE): 4586.33032130712
Root Mean Squared Error (RMSE): 67.72245064457665
Mean Absolute Error (MAE): 52.55432585541806
R-squared (R²): 0.13435278206381673

Visualizar resultados

Subtask:

Graficar un scatterplot de los valores observados vs predichos para el conjunto de prueba.

Reasoning: Plot a scatterplot of observed vs. predicted values for the test set, including a diagonal line for reference.

import matplotlib.pyplot as plt

plt.figure(figsize=(8, 6))
plt.scatter(y_test, y_pred, alpha=0.5)
plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'r--', lw=2)
plt.xlabel('Observed Values')
plt.ylabel('Predicted Values')
plt.title('Observed vs. Predicted Values')
plt.show()

Summary:

Data Analysis Key Findings

• The diabetes dataset was successfully loaded and split into 80% training data and 20% testing data.
• Features were scaled using StandardScaler.
• An MLPRegressor was trained, but a ConvergenceWarning was observed, suggesting the model did not fully converge within the specified 500 iterations.
• The model's performance on the test set resulted in a Mean Squared Error (MSE) of 4586.33, a Root Mean Squared Error (RMSE) of 67.72, a Mean Absolute Error (MAE) of 52.55, and an R-squared (R²) value of 0.13.
• A scatterplot comparing observed and predicted values showed the spread of predictions relative to the true values and a line representing perfect prediction.

Insights or Next Steps

• The low R² value (0.13) suggests that the current MLP model explains only a small portion of the variance in the target variable, indicating poor performance.
• Further steps should include addressing the convergence warning by increasing max_iter or tuning hyperparameters to improve the model's fit and potentially its evaluation metrics.