Impara Prediction | Building and Training Model

Once we've trained our module, it's time to think about test data evaluation and future predictions. We can make predictions using the method predict().

Let's look at an example. Prediction for flavanoids when the number of total phenols is 1:


              12
            
new_total_phenols = np.array([1]).reshape(-1,1)
print(model.predict(new_total_phenols))

Output: 
[2.06352084]

Method .reshape() gives a new shape to an array without changing its data. Input must be a 2-dimension (DataFrame or 2-dimension array will work here).

This value is the same as if we had substituted the line b + kx, where b is the estimated intersection with the model, and k is the slope. Please note that we multiply by 1 since the number of total phenols is 1 (x = 1).


              1
            
prediction = model.intercept_ + model.coef_*1

We can also put our testing data to get predictions for all amounts of flavanoids:


              1
            
y_test_predicted = model.predict(X_test)

Compito

Swipe to start coding

Predict with the previous split-train data the amount of flavanoids if the total phenols is 2.

[Line #6] Import the numpy library.
[Line #26] Initialize the linear regression model.
[Line #30] Assign np.array() and number of total phenols as the parameter (2) to the variable new_total_phenols (don’t forget to use the function .reshape(-1,1)).
[Line #31] Predict amount of flavanoids
[Line #32] Print the predicted amount of flavanoids.

Soluzione

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Sezione 3. Capitolo 3

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Once we've trained our module, it's time to think about test data evaluation and future predictions. We can make predictions using the method predict().

Let's look at an example. Prediction for flavanoids when the number of total phenols is 1:


              12
            
new_total_phenols = np.array([1]).reshape(-1,1)
print(model.predict(new_total_phenols))

Output: 
[2.06352084]

Method .reshape() gives a new shape to an array without changing its data. Input must be a 2-dimension (DataFrame or 2-dimension array will work here).


              1
            
prediction = model.intercept_ + model.coef_*1

We can also put our testing data to get predictions for all amounts of flavanoids:


              1
            
y_test_predicted = model.predict(X_test)

Compito

Swipe to start coding

Predict with the previous split-train data the amount of flavanoids if the total phenols is 2.

[Line #6] Import the numpy library.
[Line #26] Initialize the linear regression model.
[Line #30] Assign np.array() and number of total phenols as the parameter (2) to the variable new_total_phenols (don’t forget to use the function .reshape(-1,1)).
[Line #31] Predict amount of flavanoids
[Line #32] Print the predicted amount of flavanoids.

Soluzione

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Tutto è chiaro?

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Sezione 3. Capitolo 3

single

Prediction

Soluzione

Awesome!

Prediction

Soluzione

Awesome!