DS midterm + endterm

  There is no high correlation between the independent variables. This indicates that there is

little or no correlation between the independent variables

• Linearity

• Independence

• multicollinearity

• No multicollinearity

• Dependence

＿ is referred to as the dependent variable, the response variable or the predicted variable

• X+Y

• Y

• X

• X1+X2

• Y1+Y2

Decision tree is type of ＿ learner:

• Lazy learners

• Eager learners

• Non-Eager learners

• Semi-Optimized-algoritms

• Instance based

Logistic regression is type of＿ learner

• Lazy learners

• Eager learners

• Non-Eager learners

• Semi-Optimized-algoritms

• Instance based

Select option of machine learning that isn't valid learning

• Promotion learning

• Supervised learning

• Unsupervised learning

• Semi-Supervised learning

• Reinforcement learning

Logistic Regression, Support Vector Machine, Decision Trees, Artificial Neural network are

example of

• Lazy learners

• Eager learners

• Non-Eager learners

• Semi-Optimized-algoritms

• Instance based

K-Nearest neighbor is type of ＿ learner:

• Lazy learners

• Eager learners

• Non-Eager learners

• Semi-Optimized-algoritms

• Semi-Instance based

＿ happens when a model unable to capture the underlying pattern of the data. These models usually have high bias and low variance

• Overfit

• Underfit

• Prediction

• Semifit

• RSS

The data have no target attribute called:

• Supervised learning

• Unsupervised learning

• Reinforcement learning

• Hyper learning

• Gradient Descent

A ＿ set is another subset of the input data to which we apply the machine learning algorithm

to see how accurately it identifies relationships between the known outcomes for the target

variable and the data sets other features.

• Training

• Testing

• Validation

• Holdout

• Hyperparameter