Challenge: Choosing the Best K Value.

As shown in the previous chapters, the model makes different predictions for different k(neighbors number) values.
When we build a model, we want to choose the k that will lead to the best performance. And in the previous chapter, we learned how to measure performance using cross-validation.
Running a loop and calculating cross-validation scores for some range of k values to choose the highest sounds like a no-brainer. And that's the most frequently used approach. sklearn has a neat class for that task.

The param_grid parameter takes a dictionary with parameter names as keys and a list of items to go through as a list. For example, to try values 1-99 for n_neighbors, you would use:

python
param_grid = {'n_neighbors': range(1, 100)}

The .fit(X, y) method leads the GridSearchCV object to find the best parameters from param_grid and re-train the model with the best parameters using the whole set.
You can then get the highest score using the .best_score_ attribute and predict new values using the .predict() method.

Opgave

Swipe to start coding

Import the GridSearchCV class.
Scale the X using StandardScaler.
Look for the best value of n_neighbors among [3, 9, 18, 27].
Initialize and train a GridSearchCV object with 4 folds of cross-validation.
Print the score of the best model.

Løsning

from sklearn.neighbors import KNeighborsClassifier

from sklearn.preprocessing import StandardScaler

import pandas as pd

from sklearn.model_selection import GridSearchCV

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b71ff7ac-3932-41d2-a4d8-060e24b00129/starwars_binary.csv')

X = df[['StarWars4_rate', 'StarWars5_rate']] # Store feature columns as `X`

y = df['StarWars6'] # Store target column as `y`

# Scale the data

scaler = StandardScaler()

X_scaled = scaler.fit_transform(X)

# Initialize a model

knn = KNeighborsClassifier()

# Print the accuracy on the test set

param_grid = {'n_neighbors': [3, 9, 18, 27]}

grid_search = GridSearchCV(knn, param_grid, cv=4).fit(X_scaled, y)

print(grid_search.best_estimator_)

print(grid_search.best_score_)

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Sektion 1. Kapitel 7

from sklearn.neighbors import KNeighborsClassifier

from sklearn.preprocessing import StandardScaler

import pandas as pd

from sklearn.model_selection import ___

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b71ff7ac-3932-41d2-a4d8-060e24b00129/starwars_binary.csv')

X = df[['StarWars4_rate', 'StarWars5_rate']] # Store feature columns as `X`

y = df['StarWars6'] # Store target column as `y`

# Scale the data

scaler = StandardScaler()

X_scaled = scaler.___(X)

# Initialize a model

knn = KNeighborsClassifier()

# Print the accuracy on the test set

param_grid = {'n_neighbors': ___}

grid_search = GridSearchCV(knn, ___, cv=___).fit(X_scaled, y)

print(grid_search.best_estimator_)

print(grid_search.___)

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