brif 1.2.0

Build decision trees and random forests for classification and regression.

Description

Build random forests for classification and regression problems. The same program is available on CRAN for R users.

Installation

For Python:

python3 -m pip install brif

For R:

install.packages('brif')

Examples

import brif
import pandas as pd

# Create a brif object with default settings
bf = brif.brif()  

# Display the current parameter values. Parameter meanings are documented in the 'brif' package in R. 
bf.get_param()  

# To change certain parameter values, e.g.:
bf.set_param({'ntrees':100, 'nthreads':2})

# Or simply:
bf.ntrees = 200

# Load input data frame. Data must be a pandas data frame with appropriate headers.
# Header text and factor variable values must not contain any white space.
df = pd.read_csv("auto.csv")

# Train the model
bf.fit(df, 'origin')  # specify the target column name

# Or equivalently
bf.fit(df, 7)  # specify the target column index

# Make predictions 
# The target variable column must be excluded, and all other columns should appear in the same order as in training
# Here, predict the first 10 rows of df
pred_labels = bf.predict(df.iloc[0:10, 0:7], type='class')  # return a list containing the predicted class labels
pred_scores = bf.predict(df.iloc[0:10, 0:7], type='score')  # return a data frame containing predicted probabilities by class

# Note: for a regression problem (i.e., when the response variable is numeric type), the predict function will always return a list containing the predicted values