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funModeling (version 1.6.6)

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

2,051

Version

1.6.6

License

GPL-2

Maintainer

Pablo Casas

Last Published

December 18th, 2017

Functions in funModeling (1.6.6)

Cross-plotting input variable vs. target variable

correlation_table

Get correlation against target variable

Compare two data frames by keys

People with flu data

Fibonacci series

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

convert_df_to_categoric

Convert every column in a data frame to character

Discretize a data frame

Coordinate plot

desc_groups_rank

Profiling categorical variable (rank)

concatenate_n_vars

Concatenate 'N' variables

Plotting numerical data

Reduce cardinality in categorical variable by automatic grouping

Correlation plots

Profiling analysis of categorical vs. target variable

Outliers Data Preparation

Profiling numerical data

Profiling categorical variable

Heart Disease Data

discretize_get_bins

Get the data frame thresholds for discretization

Computes several information theory metrics between two vectors

Compare two vectors

Equal frequency binning

Importance variable ranking based on information theory

information_gain

Information gain

Filtering variables by string name

Frequency table for categorical variables

model_performance

Get model perfomance metrics (KS, AUC and ROC)

Sampling training and test data

Transform a variable into the [0-1] range

Hampel Outlier Threshold

Tukey Outlier Threshold

Computes the entropy between two variables

Generates lift and cumulative gain performance table and plot