- Multi target regression is the term used when there are multiple dependent variables.
- If the target variables are categorical, then it is called multi-label or multi-target classification, and if the target variables are numeric, then multi-target (or multi-output) regression is the name commonly used.

Is independent variable target variable? Alternative names for independent variables (especially in data mining and predictive modeling) are input variables, predictors or features. Dependent variables are also called response variables, outcome variables, target variables or output variables.

**Contents**hide

Accordingly, Is the target variable the same as the dependent variable? The target variable (also called the dependent variable) used in the analysis for this tutorial is a categorical variable that differentiates clinicians in the study that indicated that they would be Highly Likely to Use Product X from those that had a Low Likelihood to Use the new product.

**What are the two types of target variables for predictive modeling?**

Linear regression is to be used when the target variable is continuous and the dependent variable(s) is continuous or a mixture of continuous and categorical, and the relationship between the independent variable and dependent variables are linear.

What are some examples of independent and dependent variables? The type of soda – diet or regular – is the independent variable. The level of blood sugar that you measure is the dependent variable – it changes depending on the type of soda.

**How do you change the target variable in regression?**

The two approaches to applying data transforms to target variables.

… ** It involves the following steps: **

- Create the transform object, e.g. a MinMaxScaler.
- Fit the transform on the training dataset.
- Apply the transform to the train and test datasets.
- Invert the transform on any predictions made.

**What’s another word for dependent variable?**

Depending on the context, a dependent variable is sometimes called a “response variable”, “regressand”, “criterion”, “predicted variable”, “measured variable”, “explained variable”, “experimental variable”, “responding variable”, “outcome variable”, “output variable”, “target” or “label”.

**What is target attribute in data science?**

The target of a supervised model is a special kind of attribute. The target column in the training data contains the historical values used to train the model. The target column in the test data contains the historical values to which the predictions are compared.

**What is another name for dependent variable?**

Dependent variables are also called response variables, outcome variables, target variables or output variables. The terms “dependent” and “independent” here have no direct relation to the concept of statistical dependence or independence of events.

**Should I scale target variable?**

Yes, you do need to scale the target variable. I will quote this reference: A target variable with a large spread of values, in turn, may result in large error gradient values causing weight values to change dramatically, making the learning process unstable.

**Can you have two target variables?**

Multi target regression is the term used when there are multiple dependent variables. If the target variables are categorical, then it is called multi-label or multi-target classification, and if the target variables are numeric, then multi-target (or multi-output) regression is the name commonly used.

**Should I scale my dependent variable?**

Commonly, we scale all the features to the same range (e.g. 0 – 1). In addition, remember that all the values you use to scale your training data must be used to scale the test data. As for the dependent variable y you do not need to scale it.

**How do you standardize a variable?**

Typically, to standardize variables, you calculate the mean and standard deviation for a variable. Then, for each observed value of the variable, you subtract the mean and divide by the standard deviation.