# AI News, scikit-learn video #5: Choosing a machine learning model

## scikit-learn video #5: Choosing a machine learning model

Welcome back to my video series on machine learning in Python with scikit-learn.

An overfit model, like the one pictured above, has learned the noise in the data (the green line) rather than the signal (the black line).

If you want to understand this week's material at a deeper level, I strongly recommend that you review the two resources below on the bias-variance tradeoff.

It's a critical topic that shows up throughout machine learning, and will help you to gain an intuitive sense for why models behave the way they do.

In the next video, we'll learn our first technique for modeling regression problems, in which the goal is to predict a continuous response value.

## Predict an answer with a simple model

Learn how to create a simple regression model to predict the price of a diamond in Data Science for Beginners video 4.

I take a notepad and pen into the jewelry store, and I write down the price of all of the diamonds in the case and how much they weigh in carats.

Each column has a different attribute - weight in carats and price - and each row is a single data point that represents a single diamond.

Notice that it meets our criteria for quality: Now we&#39;ll pose our question in a sharp way: &quot;How much will it cost to buy a 1.35 carat diamond?&quot;

Our list doesn&#39;t have a 1.35 carat diamond in it, so we&#39;ll have to use the rest of our data to get an answer to the question.

The range of the weights is 0 to 2, so we&#39;ll draw a line that covers that range and put ticks for each half carat.

Next we&#39;ll draw a vertical axis to record the price and connect it to the horizontal weight axis.

Data scientists explain this by saying that there&#39;s the model - that&#39;s the line - and then each dot has some noise or variance associated with it.

There&#39;s the underlying perfect relationship, and then there&#39;s the gritty, real world that adds noise and uncertainty.

This envelope is called our confidence interval: We&#39;re pretty confident that prices fall within this envelope, because in the past most of them have.

Now we can say something about our confidence interval: We can say confidently that the price of a 1.35 carat diamond is about \$10,000 - but it might be as low as \$8,000 and it might be as high as \$12,000.

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