2  Cómo funciona el aprendizaje supervisado

Veremos el caso de las máquinas de soporte vectorial (SVM) para clasificación.

• Paso #1: Cargar librerías

import matplotlib.pyplot as plt

from sklearn import svm
from sklearn.datasets import make_blobs
from sklearn.inspection import DecisionBoundaryDisplay

from scipy.stats import distributions
from numpy import sum
import numpy as np

• Paso #2: Crear datos

Se crean 40 puntos usando la función make_blobs. Esta crea un conjunto de puntos separados en dos grupos.

X, y = make_blobs(n_samples=40, centers=2, random_state=6)

• Paso #3: Crear el modelo

clf = svm.SVC(kernel="linear", C=1000)

• Paso #4: Entrenar el modelo

clf.fit(X, y)

SVC(C=1000, kernel='linear')

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• Paso #5: Visualizar el modelo

plt.scatter(X[:, 0], X[:, 1], c=y, s=30, cmap=plt.cm.Paired)

# plot the decision function
ax = plt.gca()
DecisionBoundaryDisplay.from_estimator(
    clf,
    X,
    plot_method="contour",
    colors="k",
    levels=[-1, 0, 1],
    alpha=0.5,
    linestyles=["--", "-", "--"],
    ax=ax,
)
# plot support vectors
ax.scatter(
    clf.support_vectors_[:, 0],
    clf.support_vectors_[:, 1],
    s=100,
    linewidth=1,
    facecolors="none",
    edgecolors="k",
)
plt.show()

• Referencias

  1. https://scikit-learn.org/stable/modules/svm.html#
  2. https://scikit-learn.org/stable/auto_examples/svm/plot_separating_hyperplane.html#sphx-glr-auto-examples-svm-plot-separating-hyperplane-py

3 Estimación de parametros bayesiano

alpha = 10
beta = 10
n = 20
Nsamp = 201  # no of points to sample at
p = np.linspace(0, 1, Nsamp)
deltap = 1./(Nsamp-1)  # step size between samples of p

prior = distributions.beta.pdf(p, alpha, beta)

for i in range(1, 9):

    r = 2**i
    n = (3.0/2.0)*r
    like = distributions.binom.pmf(r, n, p)
    like = like/(deltap*sum(like))  # for plotting convenience only
    post = distributions.beta.pdf(p, alpha+r, beta+n-r)

    # make the figure
    plt.figure()
    plt.plot(p, post, 'k', label='posterior')
    plt.plot(p, like, 'r', label='likelihood')
    plt.plot(p, prior, 'b', label='prior')
    plt.xlabel('p')
    plt.ylabel('PDF')
    plt.legend(loc='best')
    plt.title('r/n={}/{:.0f}'.format(r, n))
    plt.show()