Question

Define a function that trains a KNN classifier and returns the accuracy of the model, the classifier model, and the minmax scaler. The parameters of the function should be the dataframe, a list of features found in the data frame, the name of the column with the labels (target), and the number of neighbors to consider.

Answer 1

To define a function that trains a KNN classifier and returns the accuracy of the model, the classifier model, and the minmax scaler, you can use the scikit-learn library in Python.

Step-by-step explanation:

To define a function that trains a KNN classifier and returns the accuracy of the model, the classifier model, and the minmax scaler, you can use the scikit-learn library in Python. Here's an example:

from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import accuracy_score

def train_KNN_classifier(dataframe, features, target_col, n_neighbors):
X = dataframe[features]
y = dataframe[target_col]
scaler = MinMaxScaler()
X_scaled = scaler.fit_transform(X)
knn = KNeighborsClassifier(n_neighbors=n_neighbors)
knn.fit(X_scaled, y)
y_pred = knn.predict(X_scaled)
accuracy = accuracy_score(y, y_pred)
return accuracy, knn, scaler

This function takes the dataframe, list of features, target column, and number of neighbors as parameters. It first scales the features using MinMaxScaler, then trains a KNN classifier on the scaled data. Finally, it returns the accuracy of the model, the classifier model, and the minmax scaler.