Machine Learning Concepts: From Data to Models · Lesson 1 · 3 minutes
Input features
Identify features in a dataset
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Facts for a computer
A model needs facts to learn. These facts are called input features. A fruit's weight or color is a feature.
Which of these is an input feature for a fruit model?
- The fruit's color
- The final fruit name
Answer: The fruit's color
Features are the facts and details you give to the model.
Features are inputs
Features are the clues you give to a model. The model reads the features to make a guess.
Complete the sentence.
Input features are the ___ you give to a model.
Answer: Input features are the facts you give to a model.
Input features provide the facts that a model inspects.
Build the rule about input features.
Answer: Measured facts the model make a guess
Input features are facts fed into a model to help it predict.
A model looks at a photo of an apple. Which item is an input feature?
- The weight of 150 grams
- The word 'Apple'
Answer: The weight of 150 grams
Features are the input facts (like weight), not the correct answer label.
Put the steps in order when giving features to a model.
Answer: Measure the fruit color → Measure the fruit weight → Pass both features into the model
You first measure features before passing them to the model.
Complete the summary.
To help a model learn, we feed it input ___ such as size.
Answer: To help a model learn, we feed it input features such as size.
Color and size are examples of input features.
What to remember
- Input features are facts and details fed into a model.
- Examples of features include width, weight, and color.
- Features are clues for the model, not the final answers.
Next in this course: Target labels. It carries on in the Breni app.
The full Machine Learning Concepts: From Data to Models path