Aprender los conceptos clave

1

Todo lo que aprenderás sobre MA con Scikit-Learn

2

¿Cómo aprenden las máquinas?

3

Problemas que podemos resolver con Scikit-learn

4

Las matemáticas que vamos a necesitar

Iniciar un proyecto con sklearn

5

Configuración de nuestro entorno Python

6

Instalación de librerías en Python

7

Datasets que usaremos en el curso

Optimización de features

8

¿Cómo afectan nuestros features a los modelos de Machine Learning?

9

Introducción al PCA

10

Preparación de datos para PCA e IPCA

11

Implementación del algoritmo PCA e IPCA

12

Kernels y KPCA

13

¿Qué es la regularización y cómo aplicarla?

14

Implementación de Lasso y Ridge

15

Explicación resultado de la implementación

16

ElasticNet: Una técnica intermedia

Regresiones robustas

17

El problema de los valores atípicos

18

Regresiones Robustas en Scikit-learn

19

Preparación de datos para la regresión robusta

20

Implementación regresión robusta

Métodos de ensamble aplicados a clasificación

21

¿Qué son los métodos de ensamble?

22

Preparación de datos para implementar métodos de ensamble

23

Implementación de Bagging

24

Implementación de Boosting

Clustering

25

Estrategias de Clustering

26

Implementación de Batch K-Means

27

Implementación de Mean-Shift

Optimización paramétrica

28

Validación de nuestro modelo usando Cross Validation

29

Implementación de K-Folds Cross Validation

30

Optimización paramétrica

31

Implementación de Randomized

32

Bonus: Auto Machine Learning

Salida a producción

33

Revisión de nuestra arquitectura de código

34

Importar y exportar modelos con Sklearn

35

Creación de una API con Flask para el modelo

36

Cierre del curso

37

Material adicional para consultar

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Implementación de Boosting

24/37
Recursos

Aportes 13

Preguntas 4

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Para este método de ensamble, jugue un poco con los estimators:

X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.35)

    estimators = range(10, 200, 10)
    total_accuracy = []
    for i in estimators:
        boost = GradientBoostingClassifier(n_estimators=i).fit(X_train, y_train)
        boost_pred = boost.predict(X_test)

        total_accuracy.append(accuracy_score(y_test, boost_pred))
    
    plt.plot(estimators, total_accuracy)
    plt.xlabel('Estimators')
    plt.ylabel('Accuracy')
    plt.show()
    plt.savefig('Boost.png')

    print(np.array(total_accuracy).max())

Accuracy = 0.9749303621169917

Configurando el boosting de esta manera:

boosting = GradientBoostingClassifier(loss='exponential',learning_rate=0.15, n_estimators=100, max_depth=5).fit(x_train, y_train)

Obtuve un score de:
0.9888579387186629

Mi intento de cross validation, al final deje n_estimators en 3 con 81%.

Para determinar el mejor n_estimator

estimators = range(10, 300, 10)
total_accuracy = []
best_result = {'result' : 0, 'n_estimator': 1}

for i in estimators:
    boost = GradientBoostingClassifier(n_estimators=i).fit(X_train, y_train)
    boost_pred = boost.predict(X_test)
    new_accuracy = accuracy_score(boost_pred, y_test)
    total_accuracy.append(new_accuracy)
    if new_accuracy > best_result['result']: 
        best_result['result'] = new_accuracy
        best_result['n_estimator'] = i

print(best_result)

investoge que es el cross validadtion para encontrar el n_estimators correcto, básicamente es dividir los datos en bloques y utilizar cada bloque como testing y el resto como training en cada iteration, con lo que se obtiene un valor de como se comporta el modelo con cualquier combinacion de los datos.

con este indicador se pueden compara modelos entre si o en este caso valores para un parametro, si quieren profundizar les recomiendo este video.

Les adjunto mi version del codigo:

import pandas as pd

from sklearn.ensemble import GradientBoostingClassifier

from sklearn.model_selection import train_test_split
from sklearn.metrics import  accuracy_score

if __name__ == "__main__":
    
    path = './Boosting/data/heart.csv'
    dataset = pd.read_csv(path)

    print(dataset.head(5))
    print('')
    print(dataset['target'].describe())

    x = dataset.drop(['target'], axis=1, inplace=False)
    y = dataset['target']

    x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.35, random_state=42)

    boost = GradientBoostingClassifier(n_estimators=50).fit(x_train, y_train)
    boost_pred = boost.predict(x_test)

    print('')
    #print('Accuracy GBC:', accuracy_score(y_test, boost_pred)) # este esta bien.
    print('Accuracy GBC:', accuracy_score(boost_pred, y_test)) # este me parece que esta mal.

Donde el output es el siguiente:

Accuracy GBC: 0.935933147632312

***<u>Bagging vs Boosting </u>*** Tanto el bagging (agregación por bootstrap) como el boosting son técnicas de aprendizaje ensemble usadas en machine learning para mejorar el rendimiento de un modelo al combinar las predicciones de múltiples modelos más débiles (también llamados estimadores base). Si bien comparten el mismo objetivo, lo logran de formas diferentes: **Bagging (Agregación por Bootstrap):** * **Diversidad a través del muestreo aleatorio:** El bagging se centra en crear diversidad entre los estimadores base. Logra esto entrenando a cada estimador base en una muestra **bootstrap** diferente de los datos de entrenamiento. El bootstrap implica crear un nuevo conjunto de datos mediante el muestreo aleatorio (con reemplazo) de los datos de entrenamiento originales. Esto significa que algunos puntos de datos pueden aparecer varias veces en una muestra bootstrap, mientras que otros pueden no incluirse en absoluto. * **Entrenamiento independiente:** Cada estimador base en el bagging se entrena independientemente de los demás. No se "comunican" ni aprenden de los errores de los otros. * **Agregación de predicciones:** Finalmente, el bagging agrega las predicciones de todos los estimadores base utilizando técnicas como la votación por mayoría para clasificación o el promedio para regresión. **Boosting (Boosting de gradiente):** * **Aprendizaje secuencial:** El boosting toma un enfoque más secuencial. Entrena a los estimadores base uno tras otro, donde cada estimador posterior se enfoca en mejorar los errores cometidos por los anteriores. * **Corrección de errores:** El boosting utiliza una técnica llamada descenso del gradiente para ajustar los pesos de los estimadores base. Los pesos determinan la influencia de cada estimador en la predicción final. A los estimadores que cometen más errores se les asignan pesos más bajos, mientras que a los que cometen menos errores se les aumentan los pesos. * **Aprendizaje adaptativo:** Este proceso de enfocarse en corregir errores pasados permite que el boosting cree un ensemble más poderoso que pueda aprender patrones más complejos en los datos. **Diferencias clave y cuándo elegir cuál:** ![](<"C:\Users\USER\Documents\Platzi\Diplomas Platzi\Escuela de Data Science e Inteligencia Artificial\Pdfs e images\Scki-Learn_Professional\Bagging vs Boosting (Precision del modelo y eliminacion, coreccion de valores atipicos).png">)![](data:image/png;base64,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) **Aquí algunas pautas generales para elegir entre bagging y boosting:** * **Usa bagging:** Si te preocupa principalmente reducir la varianza y tus estimadores base ya son relativamente buenos, el bagging es una opción más simple y rápida. También es más fácil interpretar los modelos individuales en un ensemble de bagging. * **Usa boosting:** Si deseas un modelo potencialmente más poderoso que pueda reducir tanto la varianza como el sesgo, especialmente para problemas complejos, el boosting es una buena opción. Sin embargo, los algoritmos de boosting pueden ser más costosos computacionalmente y menos interpretables. En última instancia, la mejor opción depende de las características específicas de tus datos y tu problema. Es posible que desees experimentar con bagging y boosting para ver cuál funciona mejor en tu conjunto de datos.
LinearSVC
Accuracy: 0.8133704735376045
====================================================================
SVC
Accuracy: 0.6880222841225627
====================================================================
SGDClassifier
Accuracy: 0.6796657381615598
====================================================================
DecisionTreeClassifier
Accuracy: 0.9554317548746518
====================================================================
GradientBoostingClassifier
Accuracy: 0.9331476323119777

Otra gran clase 🥇🚀🔥

24. Implementación de Boosting

import pandas as pd

from sklearn.ensemble import GradientBoostingClassifier

from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import BaggingClassifier

from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

if __name__ == '__main__':
    # Load the dataset
    df = pd.read_csv('./data/heart.csv')
    print(df['target'].describe())

    # Split the dataset into features (X) and target (y)
    X = df.drop(['target'], axis=1)
    y = df['target']

    # Split the dataset into training and testing sets
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.35, random_state=42)

    # Fit a Gradient Boosting classifier to the training data and evaluate its performance on the testing data
    boost = GradientBoostingClassifier(n_estimators=50).fit(X_train, y_train)
    boost_pred = boost.predict(X_test)
    print('=' * 64)
    print('Gradient Boosting Accuracy: ', accuracy_score(y_test, boost_pred))

Epero este aporte les sirva. Estan implementados tanto sin metodo ensamble como con bagging y boosting como resultado

import pandas as pd 

from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import BaggingClassifier

from sklearn.ensemble import GradientBoostingClassifier

from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.svm import LinearSVC
from sklearn.linear_model import SGDClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier

from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

import warnings
warnings.filterwarnings("ignore")

if __name__ == '__main__':
    #dt_heart = pd.read_csv('./datasets/heart.csv')
    dt_heart = pd.read_csv('/content/drive/MyDrive/Colab Notebooks/platzi/datas/heart.csv')
    #print(dt_heart['target'].describe())

    x = dt_heart.drop(['target'], axis=1)
    y = dt_heart['target']

    x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.35, random_state=1)



    estimators = {
        'LogisticRegression' : LogisticRegression(),
        'SVC' : SVC(),
        'LinearSVC' : LinearSVC(),
        'SGD' : SGDClassifier(loss="hinge", penalty="l2", max_iter=5),
        'KNN' : KNeighborsClassifier(),
        'DecisionTreeClf' : DecisionTreeClassifier(),
        'RandomTreeForest' : RandomForestClassifier(random_state=0)
    }

    for name, estimator in estimators.items():

        estimator_class = estimator.fit(x_train, y_train)
        estimator_prediction = estimator.predict(x_test)
        print('='*64)
        print('SCORE no bagging {} : {}'.format(name, accuracy_score(knn_prediction, y_test))  )

        bag_class = BaggingClassifier(base_estimator=estimator, n_estimators=50).fit(x_train, y_train)
        bag_predict = bag_class.predict(x_test)
        print('SCORE Bagging with {} : {}'.format(name, accuracy_score(bag_predict, y_test)))

        boost = GradientBoostingClassifier(n_estimators=50).fit(x_train, y_train)
        boost_pred = boost.predict(x_test)
        print('SCORE Boosting with {} : {}'.format(name, accuracy_score(boost_pred, y_test)))

resultado :

===============================================
SCORE no bagging LogisticRegression : 0.7270194986072424
SCORE Bagging with LogisticRegression : 0.8133704735376045
SCORE Boosting with LogisticRegression : 0.924791086350975
===============================================
SCORE no bagging SVC : 0.7270194986072424
SCORE Bagging with SVC : 0.6629526462395543
SCORE Boosting with SVC : 0.924791086350975
===============================================
SCORE no bagging LinearSVC : 0.7270194986072424
SCORE Bagging with LinearSVC : 0.8245125348189415
SCORE Boosting with LinearSVC : 0.924791086350975
===============================================
SCORE no bagging SGD : 0.7270194986072424
SCORE Bagging with SGD : 0.6685236768802229
SCORE Boosting with SGD : 0.924791086350975
===============================================
SCORE no bagging KNN : 0.7270194986072424
SCORE Bagging with KNN : 0.766016713091922
SCORE Boosting with KNN : 0.924791086350975
===============================================
SCORE no bagging DecisionTreeClf : 0.7270194986072424
SCORE Bagging with DecisionTreeClf : 0.9721448467966574
SCORE Boosting with DecisionTreeClf : 0.924791086350975
===============================================
SCORE no bagging RandomTreeForest : 0.7270194986072424
SCORE Bagging with RandomTreeForest : 0.9805013927576601
SCORE Boosting with RandomTreeForest : 0.924791086350975

Código para evitar los warnings:

KNeighborsClassifier 0.711038961038961

BaggingClassifier 0.7727272727272727

RanfomForestClassifier: 1.0

GradientBoostingClassifier: 0.9448051948051948