Return the trained model Requirements The function should be named create and train knn model Parameters x train A 2 D array of the training features y train A 1 D array of the training labels k An integer representing the number of neighbors to consider The default value should be 1 Return The function should return the trained KNN model, which is trained onn the input training data and with the input value of k In , def create and train knn model ( X train, y train, k 1 ) Creates and trains a K Nearest Neighbors ( KNN ) model Parameters X train 2 D array of training features y train 1 D array of training labels k Integer, number of neighbors ( default 1 ) Returns Trained KNN model Initialize the KNN model with k neighbors Train the model with X train and y train Return the trained model return model Example usage model create and train knn model ( X train, y train, k 5 ) Return the trained model Requirements The function should be named create and train knn model Parameters x train A 2 D array of the training features y train A 1 D array of the training labels k An integer representing the number of neighbors to consider The default value should be 1 Return The function should return the trained KNN model, which is trained onn the input training data and with the input value of k In , def create and train knn model ( X train, y train, k 1 ) Creates and trains a K Nearest Neighbors ( KNN ) model Parameters X train 2 D array of training features y train 1 D array of training labels k Integer, number of neighbors ( default 1 ) Returns Trained KNN model Initialize the KNN model with k neighbors Train the model with X train and y train Return the trained model return model Example usage model create and train knn model ( X train, y train, k 5 )

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Question: Return the trained model. Requirements: The function should be named create _ and _ train _ knn _ model. Parameters: x _ train : A

Return the trained model.

Requirements:

The function should be named create

_

and

_

train

_

knn

_

model.

Parameters:

_

train : A

2

D array of the training features.

_

train : A

1

D array of the training labels.

k

: An integer representing the number of neighbors to consider. The default value should be

1 .

Return:

The function should return the trained KNN model, which is trained onn the input training data and with the input value of

k .

[]

,

def create

_

and

_

train

_

knn

_

model

(

_

train, y

_

train,

k = 1)

" " "

Creates and trains a K

-

Nearest Neighbors

(

KNN

)

model.

Parameters:

_

train:

2

D array of training features.

_

train:

1

D array of training labels.

k: Integer, number of neighbors

(

default:

1) .

Returns:

Trained KNN model.

" " "

# Initialize the KNN model with

k

neighbors

# Train the model with X

_

train and y

_

train

# Return the trained model

return model

# Example usage:

# model

=

create

_

and

_

train

_

knn

_

model

(

_

train,

{

y_{-}

train,

k = 5)

Return the trained model.

Requirements:

The function should be named create

_

and

_

train

_

knn

_

model.

Parameters:

_

train : A

2

D array of the training features.

_

train : A

1

D array of the training labels.

k

: An integer representing the number of neighbors to consider. The default value should be

1 .

Return:

The function should return the trained KNN model, which is trained onn the input training data and with the input value of

k .

[]

,

def create

_

and

_

train

_

knn

_

model

(

_

train, y

_

train,

k = 1)

" " "

Creates and trains a K

-

Nearest Neighbors

(

KNN

)

model.

Parameters:

_

train:

2

D array of training features.

_

train:

1

D array of training labels.

k: Integer, number of neighbors

(

default:

1) .

Returns:

Trained KNN model.

" " "

# Initialize the KNN model with

k

neighbors

# Train the model with X

_

train and y

_

train

# Return the trained model

return model

# Example usage:

# model

=

create

_

and

_

train

_

knn

_

model

(

_

train,

{

y_{-}

train,

k = 5)

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