Simple neural network with scikit-learn in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the neural network model from scikit-learn.

ML Python

from sklearn.neural_network import [1]

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ALinearRegression

BDecisionTreeClassifier

CKNeighborsClassifier

DMLPClassifier

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing a model that is not a neural network like LinearRegression.

Confusing MLPClassifier with other classifiers.

The MLPClassifier is the simple neural network model in scikit-learn used for classification tasks.

2fill in blank

medium

Complete the code to create a neural network model with one hidden layer of 5 neurons.

ML Python

model = MLPClassifier(hidden_layer_sizes=([1],), max_iter=1000)

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A5

B10

C3

D1

Attempts:

3 left

💡 Hint

Common Mistakes

Using an integer without tuple parentheses.

Choosing the wrong number of neurons.

The hidden_layer_sizes parameter expects a tuple. A single number like 5 means one hidden layer with 5 neurons.

3fill in blank

hard

Fix the error in the code to train the model on data X_train and y_train.

ML Python

model.[1](X_train, y_train)

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Afit

Btransform

Cpredict

Dscore

Attempts:

3 left

💡 Hint

Common Mistakes

Using predict instead of fit to train the model.

Using transform which is for data preprocessing.

The fit method trains the model on the given data.

4fill in blank

hard

Fill both blanks to predict labels for X_test and calculate accuracy score.

ML Python

predictions = model.[1](X_test)
accuracy = [2](y_test, predictions)

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Apredict

Baccuracy_score

Cfit

Dpredict_proba

Attempts:

3 left

💡 Hint

Common Mistakes

Using fit instead of predict for predictions.

Using predict_proba instead of predict for labels.

Use predict to get predictions and accuracy_score to measure accuracy.

5fill in blank

hard

Fill all three blanks to import accuracy_score, create the model, and train it.

ML Python

from sklearn.metrics import [1]
model = MLPClassifier(hidden_layer_sizes=([2],), max_iter=1000)
model.[3](X_train, y_train)

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Aaccuracy_score

B5

Cfit

Dconfusion_matrix

Attempts:

3 left

💡 Hint

Common Mistakes

Importing confusion_matrix instead of accuracy_score.

Using wrong hidden layer size or method to train.

Import accuracy_score, set hidden layer size to 5, and train the model with fit.