Credits: Forked from PyCon 2015 Scikit-learn Tutorial by Jake VanderPlas
%matplotlib inline import numpy as np import matplotlib.pyplot as plt import seaborn; from sklearn.linear_model import LinearRegression from scipy import stats import pylab as pl seaborn.set()
from IPython.display import Image Image("http://scikit-learn.org/dev/_static/ml_map.png", width=800)
Given a scikit-learn estimator object named model, the following methods are available:
model.fit() : fit training data. For supervised learning applications,
this accepts two arguments: the data X and the labels y (e.g. model.fit(X, y)).
For unsupervised learning applications, this accepts only a single argument,
the data X (e.g. model.fit(X)).model.predict() : given a trained model, predict the label of a new set of data.
This method accepts one argument, the new data X_new (e.g. model.predict(X_new)),
and returns the learned label for each object in the array.model.predict_proba() : For classification problems, some estimators also provide
this method, which returns the probability that a new observation has each categorical label.
In this case, the label with the highest probability is returned by model.predict().model.score() : for classification or regression problems, most (all?) estimators implement
a score method. Scores are between 0 and 1, with a larger score indicating a better fit.model.predict() : predict labels in clustering algorithms.model.transform() : given an unsupervised model, transform new data into the new basis.
This also accepts one argument X_new, and returns the new representation of the data based
on the unsupervised model.model.fit_transform() : some estimators implement this method,
which more efficiently performs a fit and a transform on the same input data.from sklearn.datasets import load_iris iris = load_iris() n_samples, n_features = iris.data.shape print(iris.keys()) print((n_samples, n_features)) print(iris.data.shape) print(iris.target.shape) print(iris.target_names) print(iris.feature_names)
dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names', 'filename']) (150, 4) (150, 4) (150,) ['setosa' 'versicolor' 'virginica'] ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']
import numpy as np import matplotlib.pyplot as plt # 'sepal width (cm)' x_index = 1 # 'petal length (cm)' y_index = 2 # this formatter will label the colorbar with the correct target names formatter = plt.FuncFormatter(lambda i, *args: iris.target_names[int(i)]) plt.scatter(iris.data[:, x_index], iris.data[:, y_index], c=iris.target, cmap=plt.cm.get_cmap('RdYlBu', 3)) plt.colorbar(ticks=[0, 1, 2], format=formatter) plt.clim(-0.5, 2.5) plt.xlabel(iris.feature_names[x_index]) plt.ylabel(iris.feature_names[y_index]);
The K-Nearest Neighbors (KNN) algorithm is a method used for algorithm used for classification or for regression. In both cases, the input consists of the k closest training examples in the feature space. Given a new, unknown observation, look up which points have the closest features and assign the predominant class.
from sklearn import neighbors, datasets iris = datasets.load_iris() X, y = iris.data, iris.target # create the model knn = neighbors.KNeighborsClassifier(n_neighbors=5, weights='uniform') # fit the model knn.fit(X, y) # What kind of iris has 3cm x 5cm sepal and 4cm x 2cm petal? X_pred = [3, 5, 4, 2] result = knn.predict([X_pred, ]) print(iris.target_names[result]) print(iris.target_names) print(knn.predict_proba([X_pred, ])) from fig_code import plot_iris_knn plot_iris_knn()
['versicolor'] ['setosa' 'versicolor' 'virginica'] [[0. 0.8 0.2]]
/Users/tarrysingh/opt/anaconda3/lib/python3.8/site-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.datasets.samples_generator module is deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.datasets. Anything that cannot be imported from sklearn.datasets is now part of the private API. warnings.warn(message, FutureWarning)
Note we see overfitting in the K-Nearest Neighbors model above. We'll be addressing overfitting and model validation in a later notebook.