From the video series: Introduction to machine learning with scikit-learn
jupyter notebook 04_model_training.ipynb
Image Credits: Data3classes, Map1NN, Map5NN by Agor153. Licensed under CC BY-SA 3.0
In [1]:
# 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 [2]:
# print the shapes of X and y
print X.shape
print y.shape
Step 1: Import the class you plan to use
In [3]:
from sklearn.neighbors import KNeighborsClassifier
Step 2: "Instantiate" the "estimator"
In [4]:
knn = KNeighborsClassifier(n_neighbors=1)
In [5]:
print knn
Step 3: Fit the model with data (aka "model training")
In [6]:
knn.fit(X, y)
Out[6]:
Step 4: Predict the response for a new observation
In [7]:
print(knn.predict([[3, 5, 4, 2]]))
In [8]:
X_new = [[3, 5, 4, 2], [5, 4, 3, 2]]
knn.predict(X_new)
Out[8]:
In [9]:
# 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[9]:
In [10]:
# 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[10]: