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funModeling (version 1.9)

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

1,866

Version

1.9

License

GPL-2

Maintainer

Pablo Casas

Last Published

August 26th, 2019

Functions in funModeling (1.9)

data_country

People with flu data
discretize_rgr

Variable discretization by gain ratio maximization
entropy_2

Computes the entropy between two variables
categ_analysis

Profiling analysis of categorical vs. target variable
desc_groups

Profiling categorical variable
concatenate_n_vars

Concatenate 'N' variables
data_integrity

Data integrity
funModeling-package

funModeling: Exploratory data analysis, data preparation and model performance
discretize_df

Discretize a data frame
discretize_get_bins

Get the data frame thresholds for discretization
desc_groups_rank

Profiling categorical variable (rank)
hampel_outlier

Hampel Outlier Threshold
data_golf

Play golf
gain_ratio

Gain ratio
get_sample

Sampling training and test data
tukey_outlier

Tukey Outlier Threshold
information_gain

Information gain
export_plot

Export plot to jpeg file
equal_freq

Equal frequency binning
infor_magic

Computes several information theory metrics between two vectors
status

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

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

Generates lift and cumulative gain performance table and plot
profiling_num

Profiling numerical data
plotar

Correlation plots
prep_outliers

Outliers Data Preparation
metadata_models

Metadata models data integrity
plot_num

Plotting numerical data
heart_disease

Heart Disease Data
freq

Frequency table for categorical variables
fibonacci

Fibonacci series
v_compare

Compare two vectors
var_rank_info

Importance variable ranking based on information theory
range01

Transform a variable into the [0-1] range
auto_grouping

Reduce cardinality in categorical variable by automatic grouping
convert_df_to_categoric

Convert every column in a data frame to character
correlation_table

Get correlation against target variable
coord_plot

Coordinate plot
compare_df

Compare two data frames by keys
cross_plot

Cross-plotting input variable vs. target variable