Brainome™ creates predictors using these three model types:
This notebook requires brainome as installed per notebook brainome_101_Quick_Start
The training data set used is titanic_train.csv.
!python3 -m pip install brainome --quiet
!brainome --version
import urllib.request as request
print('Downloading titanic_train.csv')
request.urlretrieve('https://download.brainome.ai/data/public/titanic_train.csv', 'titanic_train.csv')
%ls -lh titanic_train.csv
Brainome can automatically select the most appropriate model type for your data's measurements. In titanic's case, brainome selects Random Forest.
!brainome titanic_train.csv -y -o predictor_103_automatic.py
The predictor filename is predictor_103_automatic.py
. The source code is approximately 39K bytes.
%ls -lh predictor_103_automatic.py
%pycat predictor_103_automatic.py
You can select the Random Forest model type by using the -f RF
parameter.
Note: The
-modelonly
parameter bypasses the measurements phase which do not change from the previous runs.
!brainome titanic_train.csv -f RF -y -o predictor_103_RF.py -modelonly
Open predictor_103_RF.py
to view the Random Forest Predictor
%ls -lh predictor_103_RF.py
%pycat predictor_103_RF.py
You can select the Neural Network model type by using the -f NN
parameter.
!brainome titanic_train.csv -f NN -y -o predictor_103_NN.py -modelonly
Open predictor_103_NN.py
to view the Neural Network Predictor. The source code is approximately 57K bytes.
%ls -lh predictor_103_NN.py
%pycat predictor_103_NN.py
You can select the Decision Tree model type by using the -f DT
parameter
!brainome titanic_train.csv -f DT -y -o predictor_103_DT.py -modelonly
Open predictor_103_DT.py
to view the Decision Tree Predictor. The source code is approximately 33K bytes
%ls -lh predictor_103_DT.py
%pycat predictor_103_DT.py