Data Transformation

Question 1

Apply Filter

Answer 1

Data Transformation - Filter

Applies a filter to specified columns of a dataset.

Question 2

FIR Filter:

Answer 2

Data Transformation - Filter

Creates an FIR filter for signal processing.

Question 3

IIR Filter:

Answer 3

Data Transformation - Filter

Creates an IIR filter for signal processing.

Question 4

Median Filter:

Answer 4

Data Transformation - Filter

Creates a median filter that’s used to smooth data for trend analysis.

Question 5

Moving Average Filter:

Answer 5

Data Transformation - Filter

Creates a moving average filter that smooths data for trend analysis.

Question 6

Threshold Filter:

Answer 6

Data Transformation - Filter

Creates a threshold filter that constrains values.

Question 7

User-Defined Filter:

Answer 7

Data Transformation - Filter

Creates a custom FIR or IIR filter.

FIR=Creates a finite impulse response filter for signal processing

IIR=Creates an infinite impulse response filter for signal processing

Question 8

Build Counting Transform:

Answer 8

Data Transformation - Learning with Counts

Creates a count table and count-based features from a dataset, and then saves the table and features as a transformation.

Question 9

Merge Count Transform:

Answer 9

Data Transformation - Learning with Counts

Merges two sets of count-based features.

Question 10

Modify Count Table Parameters:

Answer 10

Data Transformation - Learning with Counts

Modifies count-based features that are derived from an existing count table.

Question 11

Add Rows:

Answer 11

Appends a set of rows from an input dataset to the end of another dataset.

Question 12

Add Rows:

Answer 12

Data Transformation - Manipulation

Appends a set of rows from an input dataset to the end of another dataset.

Question 13

Apply SQL Transformation:

Answer 13

Data Transformation - Manipulation

Runs a SQLite query on input datasets to transform the data.

Question 14

Clean Missing Data:

Answer 14

Data Transformation - Manipulation

Specifies how to handle values that are missing from a dataset. This module replaces Missing Values Scrubber, which has been deprecated.

Question 15

Convert to Indicator Values:

Answer 15

Data Transformation - Manipulation

Converts categorical values in columns to indicator values.

Question 16

Edit Metadata:

Answer 16

Data Transformation - Manipulation

Edits metadata that’s associated with columns in a dataset.

Question 17

Group Categorical Values:

Answer 17

Data Transformation - Manipulation

Groups data from multiple categories into a new category.

Question 18

Join Data:

Answer 18

Data Transformation - Manipulation

Joins two datasets.

Question 19

Select Columns in Dataset:

Answer 19

Data Transformation - Manipulation

Selects columns to include in a dataset or exclude from a dataset in an operation.

Question 20

Select Columns Transform:

Answer 20

Data Transformation - Manipulation

Creates a transformation that selects the same subset of columns as in a specified dataset.

Question 21

SMOTE:

Answer 21

Data Transformation - Manipulation

Increases the number of low-incidence examples in a dataset by using synthetic minority oversampling.

Question 22

Remove Duplicate Rows:

Answer 22

Data Transformation - Manipulation

Removes duplicate rows from a dataset.

Question 23

Partition and Sample:

Answer 23

Data Transformation - Sample and Split

Creates multiple partitions of a dataset based on sampling.

Question 24

Split Data:

Answer 24

Data Transformation - Sample and Split

Partitions the rows of a dataset into two distinct sets.

Question 25

Clip Values:

Answer 25

Data Transformation - Scale and Reduce

Detects outliers, and then clips or replaces their values.

Question 26

Group Data into Bins:

Answer 26

Data Transformation - Scale and Reduce

Puts numerical data into bins.

Question 27

Normalize Data:

Answer 27

Data Transformation - Scale and Reduce

Rescales numeric data to constrain dataset values to a standard range.

Question 28

Principal Component Analysis:

Answer 28

Data Transformation - Scale and Reduce / Normalize Data

Computes a set of features that have reduced dimensionality for more efficient learning.