Aprende Machine Learning Con Scikitlearn Keras Y Tensorflow [upd] < 5000+ PREMIUM >

import tensorflow as tf a = tf.constant(5) b = tf.constant(3) c = a + b Ejecutar (modo eager) print(c.numpy()) # 8

grid = GridSearchCV(estimator=modelo_keras, param_grid=param_grid, cv=3) grid.fit(X_train, y_train) print(f"Mejores parámetros: {grid.best_params_}") aprende machine learning con scikitlearn keras y tensorflow

param_grid = { "model__neurons": [32, 64, 128], "model__optimizer": ["adam", "rmsprop"], "batch_size": [16, 32] } import tensorflow as tf a = tf

from sklearn.ensemble import RandomForestClassifier from sklearn.model_segmentation import train_test_split from sklearn.metrics import accuracy_score X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) Crear y entrenar modelo modelo = RandomForestClassifier() modelo.fit(X_train, y_train) Predecir y evaluar predicciones = modelo.predict(X_test) print(f"Precisión: {accuracy_score(y_test, predicciones)}") y_test = train_test_split(X