Data Transformation Flashcards
Apply Filter
Data Transformation - Filter
Applies a filter to specified columns of a dataset.
FIR Filter:
Data Transformation - Filter
Creates an FIR filter for signal processing.
IIR Filter:
Data Transformation - Filter
Creates an IIR filter for signal processing.
Median Filter:
Data Transformation - Filter
Creates a median filter that’s used to smooth data for trend analysis.
Moving Average Filter:
Data Transformation - Filter
Creates a moving average filter that smooths data for trend analysis.
Threshold Filter:
Data Transformation - Filter
Creates a threshold filter that constrains values.
User-Defined Filter:
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
Build Counting Transform:
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.
Merge Count Transform:
Data Transformation - Learning with Counts
Merges two sets of count-based features.
Modify Count Table Parameters:
Data Transformation - Learning with Counts
Modifies count-based features that are derived from an existing count table.
Add Rows:
Appends a set of rows from an input dataset to the end of another dataset.
Add Rows:
Data Transformation - Manipulation
Appends a set of rows from an input dataset to the end of another dataset.
Apply SQL Transformation:
Data Transformation - Manipulation
Runs a SQLite query on input datasets to transform the data.
Clean Missing Data:
Data Transformation - Manipulation
Specifies how to handle values that are missing from a dataset. This module replaces Missing Values Scrubber, which has been deprecated.
Convert to Indicator Values:
Data Transformation - Manipulation
Converts categorical values in columns to indicator values.
Edit Metadata:
Data Transformation - Manipulation
Edits metadata that’s associated with columns in a dataset.
Group Categorical Values:
Data Transformation - Manipulation
Groups data from multiple categories into a new category.
Join Data:
Data Transformation - Manipulation
Joins two datasets.
Select Columns in Dataset:
Data Transformation - Manipulation
Selects columns to include in a dataset or exclude from a dataset in an operation.
Select Columns Transform:
Data Transformation - Manipulation
Creates a transformation that selects the same subset of columns as in a specified dataset.
SMOTE:
Data Transformation - Manipulation
Increases the number of low-incidence examples in a dataset by using synthetic minority oversampling.
Remove Duplicate Rows:
Data Transformation - Manipulation
Removes duplicate rows from a dataset.
Partition and Sample:
Data Transformation - Sample and Split
Creates multiple partitions of a dataset based on sampling.
Split Data:
Data Transformation - Sample and Split
Partitions the rows of a dataset into two distinct sets.
Clip Values:
Data Transformation - Scale and Reduce
Detects outliers, and then clips or replaces their values.
Group Data into Bins:
Data Transformation - Scale and Reduce
Puts numerical data into bins.
Normalize Data:
Data Transformation - Scale and Reduce
Rescales numeric data to constrain dataset values to a standard range.
Principal Component Analysis:
Data Transformation - Scale and Reduce / Normalize Data
Computes a set of features that have reduced dimensionality for more efficient learning.
