Implementing k-NN
KNeighborsClassifier
Implementing k-Nearest Neighbors is pretty straightforward. We only need to import and use the KNeighborsClassifier class.
Once you imported the class and created a class object like this:
# Importing the class
from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors=3)
You need to feed it the training data using the .fit() method:
knn.fit(X_scaled, y)
And that's it! You can predict new values now.
y_pred = knn.predict(X_new_scaled)
Scaling the data
However, remember that the data must be scaled. StandardScaler is commonly used for this purpose:
You must compute xΛ and s only on the training set using .fit() or .fit_transform().
Then use .transform() on the test set so both sets are scaled identically:
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)
Using different scaling values for train and test harms predictions.
Example
We predict whether a person enjoys Star Wars VI using their ratings for Episodes IV and V (from The Movies Dataset).
After training, we test two users: one rated IV/V as 5 and 5, the other as 4.5 and 4.
123456789101112131415161718192021222324252627from sklearn.neighbors import KNeighborsClassifier from sklearn.preprocessing import StandardScaler import numpy as np import pandas as pd import warnings warnings.filterwarnings('ignore') df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b71ff7ac-3932-41d2-a4d8-060e24b00129/starwars_binary.csv') # Dropping the target column and leaving only features as `X_train` X_train = df.drop('StarWars6', axis=1) # Storing target column as `y_train`, which contains 1 (liked SW 6) or 0 (didn't like SW 6) y_train = df['StarWars6'] # Test set of two people X_test = np.array([[5, 5], [4.5, 4]]) # Scaling the data scaler = StandardScaler() X_train = scaler.fit_transform(X_train) X_test = scaler.transform(X_test) # Building a model and predict new instances knn = KNeighborsClassifier(n_neighbors=13).fit(X_train, y_train) y_pred = knn.predict(X_test) print(y_pred)
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KNeighborsClassifier
Implementing k-Nearest Neighbors is pretty straightforward. We only need to import and use the KNeighborsClassifier class.
Once you imported the class and created a class object like this:
# Importing the class
from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors=3)
You need to feed it the training data using the .fit() method:
knn.fit(X_scaled, y)
And that's it! You can predict new values now.
y_pred = knn.predict(X_new_scaled)
Scaling the data
However, remember that the data must be scaled. StandardScaler is commonly used for this purpose:
You must compute xΛ and s only on the training set using .fit() or .fit_transform().
Then use .transform() on the test set so both sets are scaled identically:
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)
Using different scaling values for train and test harms predictions.
Example
We predict whether a person enjoys Star Wars VI using their ratings for Episodes IV and V (from The Movies Dataset).
After training, we test two users: one rated IV/V as 5 and 5, the other as 4.5 and 4.
123456789101112131415161718192021222324252627from sklearn.neighbors import KNeighborsClassifier from sklearn.preprocessing import StandardScaler import numpy as np import pandas as pd import warnings warnings.filterwarnings('ignore') df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b71ff7ac-3932-41d2-a4d8-060e24b00129/starwars_binary.csv') # Dropping the target column and leaving only features as `X_train` X_train = df.drop('StarWars6', axis=1) # Storing target column as `y_train`, which contains 1 (liked SW 6) or 0 (didn't like SW 6) y_train = df['StarWars6'] # Test set of two people X_test = np.array([[5, 5], [4.5, 4]]) # Scaling the data scaler = StandardScaler() X_train = scaler.fit_transform(X_train) X_test = scaler.transform(X_test) # Building a model and predict new instances knn = KNeighborsClassifier(n_neighbors=13).fit(X_train, y_train) y_pred = knn.predict(X_test) print(y_pred)
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