Introducing Memory Equivalent Capacity to measure how adaptable my model is.
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
The Memory Equivalent Capacity for a machine learner is dependent on the number of parameters used, the topology of the machine learner, the training method, as well as the training efficiency. It can be estimated as an upper limit. Measured in bits.
Example: A machine learner with 10 bits of Memory Equivalent Capacity is guaranteed to memorize any binary classification task of 10 instances or less. from the Brainome Glossary
!brainome https://download.brainome.ai/data/public/titanic_train.csv -f RF -y -o predictor_204_RF.PY | grep MEC
!brainome https://download.brainome.ai/data/public/titanic_train.csv -f DT -y -o predictor_204_DT.PY | grep MEC
!brainome https://download.brainome.ai/data/public/titanic_train.csv -f NN -y -o predictor_204_NN.PY | grep MEC
Pick the model with the least MEC.