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 should calculate xΜ (mean) and s (standard deviation) on the training set using either .fit() or .fit_transform() method. This step ensures that the scaling parameters are derived from the training data.
When you have test set to predict, you must use the same xΜ and s to preprocess this data using .transform(). This consistency is crucial because it ensures that the test data is scaled in the same way as the training data, maintaining the integrity of the model's predictions.
# Importing the class
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
# Calculating xΜ and s and scaling `X_train`
X_train_scaled = scaler.fit_transform(X_train)
# Scaling `X_test` with xΜ and s calculated in the previous line
X_test_scaled = scaler.transform(X_test)
If you use different xΜ and s for training set and test set, your predictions will likely be worse.
Example
Let's explore a straightforward example where we aim to predict whether a person will enjoy Star Wars VI based on their ratings for Star Wars IV and V. The data is taken from The Movies Dataset with extra preprocessing. A person is considered to like Star Wars VI if they rate it more than 4 (out of 5).
After training our model, we'll make predictions for two individuals from the test set. The first individual rates Star Wars IV and V as 5 and 5, respectively, while the second individual rates them 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 should calculate xΜ (mean) and s (standard deviation) on the training set using either .fit() or .fit_transform() method. This step ensures that the scaling parameters are derived from the training data.
When you have test set to predict, you must use the same xΜ and s to preprocess this data using .transform(). This consistency is crucial because it ensures that the test data is scaled in the same way as the training data, maintaining the integrity of the model's predictions.
# Importing the class
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
# Calculating xΜ and s and scaling `X_train`
X_train_scaled = scaler.fit_transform(X_train)
# Scaling `X_test` with xΜ and s calculated in the previous line
X_test_scaled = scaler.transform(X_test)
If you use different xΜ and s for training set and test set, your predictions will likely be worse.
Example
Let's explore a straightforward example where we aim to predict whether a person will enjoy Star Wars VI based on their ratings for Star Wars IV and V. The data is taken from The Movies Dataset with extra preprocessing. A person is considered to like Star Wars VI if they rate it more than 4 (out of 5).
After training our model, we'll make predictions for two individuals from the test set. The first individual rates Star Wars IV and V as 5 and 5, respectively, while the second individual rates them 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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