Training a machine learning model with scikit-learn¶

From the video series: Introduction to machine learning with scikit-learn

Agenda¶

What is the K-nearest neighbors classification model?
What are the four steps for model training and prediction in scikit-learn?
How can I apply this pattern to other machine learning models?

Reviewing the iris dataset¶

In [2]:

from IPython.display import IFrame
IFrame('http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data', width=300, height=200)

Out[2]:

150 observations
4 features (sepal length, sepal width, petal length, petal width)
Response variable is the iris species
Classification problem since response is categorical
More information in the UCI Machine Learning Repository

K-nearest neighbors (KNN) classification¶

Pick a value for K.
Search for the K observations in the training data that are "nearest" to the measurements of the unknown iris.
Use the most popular response value from the K nearest neighbors as the predicted response value for the unknown iris.

Example training data¶

Training data

KNN classification map (K=1)¶

1NN classification map

KNN classification map (K=5)¶

5NN classification map

Image Credits: Data3classes, Map1NN, Map5NN by Agor153. Licensed under CC BY-SA 3.0

Loading the data¶

In [3]:

# import load_iris function from datasets module
from sklearn.datasets import load_iris

# save "bunch" object containing iris dataset and its attributes
iris = load_iris()

# store feature matrix in "X"
X = iris.data

# store response vector in "y"
y = iris.target

In [4]:

# print the shapes of X and y
print(X.shape)
print(y.shape)

(150L, 4L)
(150L,)

scikit-learn 4-step modeling pattern¶

Step 1: Import the class you plan to use

In [5]:

from sklearn.neighbors import KNeighborsClassifier

Step 2: "Instantiate" the "estimator"

"Estimator" is scikit-learn's term for model
"Instantiate" means "make an instance of"

In [6]:

knn = KNeighborsClassifier(n_neighbors=1)

Name of the object does not matter
Can specify tuning parameters (aka "hyperparameters") during this step
All parameters not specified are set to their defaults

In [7]:

print(knn)

KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
           metric_params=None, n_jobs=1, n_neighbors=1, p=2,
           weights='uniform')

Step 3: Fit the model with data (aka "model training")

Model is learning the relationship between X and y
Occurs in-place

In [8]:

knn.fit(X, y)

Out[8]:

KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
           metric_params=None, n_jobs=1, n_neighbors=1, p=2,
           weights='uniform')

Step 4: Predict the response for a new observation

New observations are called "out-of-sample" data
Uses the information it learned during the model training process

In [9]:

knn.predict([[3, 5, 4, 2]])

Out[9]:

array([2])

Returns a NumPy array
Can predict for multiple observations at once

In [10]:

X_new = [[3, 5, 4, 2], [5, 4, 3, 2]]
knn.predict(X_new)

Out[10]:

array([2, 1])

Using a different value for K¶

In [11]:

# instantiate the model (using the value K=5)
knn = KNeighborsClassifier(n_neighbors=5)

# fit the model with data
knn.fit(X, y)

# predict the response for new observations
knn.predict(X_new)

Out[11]:

array([1, 1])

Using a different classification model¶

In [12]:

# import the class
from sklearn.linear_model import LogisticRegression

# instantiate the model (using the default parameters)
logreg = LogisticRegression()

# fit the model with data
logreg.fit(X, y)

# predict the response for new observations
logreg.predict(X_new)

Out[12]:

array([2, 0])

Resources¶

Nearest Neighbors (user guide), KNeighborsClassifier (class documentation)
Logistic Regression (user guide), LogisticRegression (class documentation)
Videos from An Introduction to Statistical Learning
- Classification Problems and K-Nearest Neighbors (Chapter 2)
- Introduction to Classification (Chapter 4)
- Logistic Regression and Maximum Likelihood (Chapter 4)

Comments or Questions?¶

In [1]:

from IPython.core.display import HTML
def css_styling():
    styles = open("styles/custom.css", "r").read()
    return HTML(styles)
css_styling()

Out[1]: