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]

Drag options to blanks, or click blank then click option'

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)

Drag options to blanks, or click blank then click option'

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)

Drag options to blanks, or click blank then click option'

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)

Drag options to blanks, or click blank then click option'

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)

Drag options to blanks, or click blank then click option'

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.

Practice

1. What is the main purpose of using MLPClassifier in scikit-learn?

easy

A. To perform data scaling and normalization

B. To create and train a simple neural network for classification tasks

C. To visualize data distributions

D. To split data into training and testing sets

Solution

1. Step 1: Understand the role of MLPClassifier

   MLPClassifier is a tool in scikit-learn designed to build and train neural networks for classification problems.

2. Step 2: Compare options with MLPClassifier's purpose

   The other options describe tasks like scaling, visualization, and data splitting, which are not the main function of MLPClassifier.

3. Final Answer:

   To create and train a simple neural network for classification tasks -> Option B

4. Quick Check:

   MLPClassifier = neural network classifier [OK]

Hint: MLPClassifier builds neural nets for classification [OK]

Common Mistakes:

• Confusing MLPClassifier with data preprocessing tools
• Thinking it is for regression only
• Assuming it splits data automatically

2. Which of the following is the correct way to import the MLPClassifier from scikit-learn?

easy

A. import neural_network.MLPClassifier from sklearn

B. import MLPClassifier from sklearn.network

C. from sklearn.ml import MLPClassifier

D. from sklearn.neural_network import MLPClassifier

Solution

1. Step 1: Recall the correct module path for MLPClassifier

   MLPClassifier is located in the sklearn.neural_network module.

2. Step 2: Check each import statement for syntax and correctness

   The other options have incorrect module names or import syntax.

3. Final Answer:

   from sklearn.neural_network import MLPClassifier -> Option D

4. Quick Check:

   Correct import = from sklearn.neural_network import MLPClassifier [OK]

Hint: MLPClassifier is in sklearn.neural_network module [OK]

Common Mistakes:

• Using wrong module names like sklearn.ml
• Incorrect import syntax
• Confusing neural_network with network

3. What will be the output of the following code snippet?

from sklearn.neural_network import MLPClassifier
X = [[0., 0.], [1., 1.]]
y = [0, 1]
clf = MLPClassifier(hidden_layer_sizes=(2,), max_iter=1000, random_state=1)
clf.fit(X, y)
pred = clf.predict([[2., 2.]])
print(pred)

medium

A. [0]

B. Error due to wrong input shape

C. [1]

D. [2]

Solution

1. Step 1: Understand the training data and labels

   The model is trained on two points: [0., 0.] labeled 0 and [1., 1.] labeled 1.

2. Step 2: Predict the label for input [2., 2.]

   Since [2., 2.] is closer to [1., 1.] which has label 1, the model predicts 1.

3. Final Answer:

   [1] -> Option C

4. Quick Check:

   Input near class 1 predicts 1 [OK]

Hint: Prediction matches closest trained label [OK]

Common Mistakes:

• Assuming output is 0 by default
• Confusing input shape errors
• Ignoring random_state effect

4. Identify the error in the following code snippet:

from sklearn.neural_network import MLPClassifier
X = [[0, 0], [1, 1]]
y = [0, 1]
clf = MLPClassifier(hidden_layer_sizes=2, max_iter=500)
clf.fit(X, y)

medium

A. hidden_layer_sizes should be a tuple, not an integer

B. max_iter value is too low

C. X and y have mismatched lengths

D. MLPClassifier requires data scaling before fitting

Solution

1. Step 1: Check the hidden_layer_sizes parameter

   hidden_layer_sizes expects a tuple indicating the number of neurons per hidden layer, e.g., (2,), not an integer.

2. Step 2: Verify other parameters and data

   max_iter=500 is valid, X and y lengths match, and scaling is recommended but not mandatory.

3. Final Answer:

   hidden_layer_sizes should be a tuple, not an integer -> Option A

4. Quick Check:

   hidden_layer_sizes = tuple [OK]

Hint: hidden_layer_sizes must be tuple like (2,) [OK]

Common Mistakes:

• Passing integer instead of tuple for hidden_layer_sizes
• Assuming max_iter causes error
• Thinking scaling is mandatory for fit

5. You want to improve your MLPClassifier model's accuracy on a complex dataset. Which of these changes is most likely to help?

hard

A. Increase the number of neurons in hidden layers and train longer

B. Reduce the number of hidden layers to zero

C. Remove the activation function to speed up training

D. Use fewer training samples to avoid overfitting

Solution

1. Step 1: Understand model complexity and training

   Increasing neurons and training longer allows the model to learn more complex patterns.

2. Step 2: Evaluate other options

   Reducing hidden layers to zero removes learning capacity, removing activation breaks non-linearity, and fewer samples usually reduce accuracy.

3. Final Answer:

   Increase the number of neurons in hidden layers and train longer -> Option A

4. Quick Check:

   More neurons + training = better learning [OK]

Hint: More neurons and training improve complex learning [OK]

Common Mistakes:

• Thinking fewer layers always improve accuracy
• Removing activation functions to simplify
• Using less data to prevent overfitting blindly