Challenge: Evaluating the Perceptron
To evaluate the previously created perceptron, you will use a dataset containing two input features and two distinct classes (0
and 1
):
This dataset is balanced, with 500 samples from class 1
and 500 samples from class 0
. Therefore, accuracy is a sufficient metric for evaluation in this case, which can be calculated using the accuracy_score()
function:
accuracy_score(y_true, y_pred)
y_true
represents the actual labels, while y_pred
represents the predicted labels.
The dataset is stored in perceptron.py
as two NumPy arrays: X
(input features) and y
(corresponding labels), so they will be simply imported. This file also contains model
, which is the instance of the Perceptron
class you previously created.
Swipe to start coding
Obtain predictions from the trained model and evaluate its performance:
- Split the dataset into training (80%) and testing (20%) sets.
- Train the model for 10 epochs with a learning rate of
0.01
. - Obtain predictions for all examples in the test set.
- Calculate the accuracy by comparing the predicted labels with the actual test labels.
Lösning
Tack för dina kommentarer!
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Challenge: Evaluating the Perceptron
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To evaluate the previously created perceptron, you will use a dataset containing two input features and two distinct classes (0
and 1
):
This dataset is balanced, with 500 samples from class 1
and 500 samples from class 0
. Therefore, accuracy is a sufficient metric for evaluation in this case, which can be calculated using the accuracy_score()
function:
accuracy_score(y_true, y_pred)
y_true
represents the actual labels, while y_pred
represents the predicted labels.
The dataset is stored in perceptron.py
as two NumPy arrays: X
(input features) and y
(corresponding labels), so they will be simply imported. This file also contains model
, which is the instance of the Perceptron
class you previously created.
Swipe to start coding
Obtain predictions from the trained model and evaluate its performance:
- Split the dataset into training (80%) and testing (20%) sets.
- Train the model for 10 epochs with a learning rate of
0.01
. - Obtain predictions for all examples in the test set.
- Calculate the accuracy by comparing the predicted labels with the actual test labels.
Lösning
Tack för dina kommentarer!
Awesome!
Completion rate improved to 4single