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funModeling (version 1.6.8)

Exploratory Data Analysis and Data Preparation Tool-Box Book

Description

Around 10% of almost any predictive modeling project is spent in predictive modeling, 'funModeling' and the book Data Science Live Book () are intended to cover remaining 90%: data preparation, profiling, selecting best variables 'dataViz', assessing model performance and other functions.

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Version

Install

install.packages('funModeling')

Monthly Downloads

1,528

Version

1.6.8

License

GPL-2

Maintainer

Pablo Casas

Last Published

July 31st, 2018

Functions in funModeling (1.6.8)

correlation_table

Get correlation against target variable

Cross-plotting input variable vs. target variable

Compare two data frames by keys

Get a summary for the given data frame (o vector).

concatenate_n_vars

Concatenate 'N' variables

Discretize a data frame

Plotting numerical data

Fibonacci series

Correlation plots

Transform a variable into the [0-1] range

Tukey Outlier Threshold

Frequency table for categorical variables

Reduce cardinality in categorical variable by automatic grouping

Hampel Outlier Threshold

Heart Disease Data

Sampling training and test data

Profiling analysis of categorical vs. target variable

Profiling categorical variable

desc_groups_rank

Profiling categorical variable (rank)

convert_df_to_categoric

Convert every column in a data frame to character

Coordinate plot

Equal frequency binning

Export plot to jpeg file

Computes several information theory metrics between two vectors

information_gain

Information gain

Compare two vectors

Importance variable ranking based on information theory

People with flu data

discretize_get_bins

Get the data frame thresholds for discretization

Computes the entropy between two variables

funModeling-package

funModeling: Exploratory data analysis, data preparation and model performance

Generates lift and cumulative gain performance table and plot

Profiling numerical data

Outliers Data Preparation