Binary classification model in TensorFlow - Cheat Sheet & Quick Revision

Recall & Review

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What is a binary classification model?

A binary classification model is a type of machine learning model that sorts data into two groups or classes, like deciding if an email is spam or not spam.

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Which activation function is commonly used in the output layer of a binary classification model?

The sigmoid activation function is commonly used because it outputs values between 0 and 1, which can be interpreted as probabilities for the two classes.

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What loss function is typically used to train a binary classification model?

Binary cross-entropy loss is typically used because it measures how well the predicted probabilities match the actual class labels (0 or 1).

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Why do we use accuracy as a metric in binary classification?

Accuracy tells us the percentage of correct predictions out of all predictions, helping us understand how well the model is performing overall.

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What does the output of a binary classification model represent?

The output is a probability score between 0 and 1 that shows how likely the input belongs to the positive class. We usually pick class 1 if the score is above 0.5.

Which activation function is best for the output layer in a binary classification model?

A ReLU

B Sigmoid

C Softmax

D Tanh

Sigmoid outputs values between 0 and 1, perfect for binary classification probabilities.

What does binary cross-entropy loss measure?

A Difference between predicted probabilities and actual labels

B Distance between data points

C Sum of squared errors

D Number of correct predictions

Binary cross-entropy measures how close predicted probabilities are to the true class labels.

If a model outputs 0.8 for a sample, what class is predicted assuming a 0.5 threshold?

A Cannot decide

B Class 0

C Class 1

D Class 2

Since 0.8 is greater than 0.5, the model predicts class 1.

Which metric shows the percentage of correct predictions?

A Loss

B Recall

C Precision

D Accuracy

Accuracy measures the ratio of correct predictions to total predictions.

What is the typical shape of the output layer in a binary classification model?

A One neuron with sigmoid activation

B Two neurons with softmax activation

C Multiple neurons with ReLU activation

D One neuron with linear activation

Binary classification usually uses one neuron with sigmoid to output a probability.

Explain how a binary classification model works from input to output.

Think about how data flows and how the model decides between two classes.

Describe the role of the loss function and metric in training a binary classification model.

Consider what guides learning and what tells us how well the model is doing.