Last Updated on October 22, 2021 by Admin

## DRAG DROP

## You need to correct the model fit issue.

## Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

**Explanation:**

**Step 1:** Augment the data

Scenario: Columns in each dataset contain missing and null values. The datasets also contain many outliers.

**Step 2:** Add the Bayesian Linear Regression module.

Scenario: You produce a regression model to predict property prices by using the Linear Regression and Bayesian Linear Regression modules.

**Step 3:** Configure the regularization weight.

Regularization typically is used to avoid overfitting. For example, in L2 regularization weight, type the value to use as the weight for L2 regularization. We recommend that you use a non-zero value to avoid overfitting.

**Scenario:**

Model fit: The model shows signs of overfitting. You need to produce a more refined regression model that reduces the overfitting.

**Incorrect Answers:**

Multiclass Decision Jungle module:

Decision jungles are a recent extension to decision forests. A decision jungle consists of an ensemble of decision directed acyclic graphs (DAGs).

**L-BFGS:**

L-BFGS stands for “limited memory Broyden-Fletcher-Goldfarb-Shanno”. It can be found in the wwo-Class Logistic Regression module, which is used to create a logistic regression model that can be used to predict two (and only two) outcomes.