ML Regression Model Building using Feature Input¶
‘ML Regression Model Building using Feature Input’ is a floe that train multiple Neural Network Regression models on custom feature input provided by the user. Every input training record needs to contain a feature vector as FloatVec which will be used to train the models. These feature vectors will be normalized before training. It builds Machine Learning models for all possible combination of neural network hyperparameters provided below. Generates floe report containing details of the best models built. User can pick any model and use it to predict properties of other molecules in a separate floe (Predict Physical Properties). The floe report presents detailed statistics on the hyperparameters so as to tweak them and build better models (See documentation). In addition to prediction, the built models provide explanation of predictions and confidence interval. NOTE: This floe by default, builds about 1k machine learning models. Although cheaper than generating fingerprints, on a large dataset, this maybe pricey. Refer to documentations on how to build a cheaper version of the same
Name |
Description |
Type |
---|---|---|
Input Small Molecules to train
machine learning models on.
|
Input dataset file with each record containing
molecule and response value(float) to train on
|
Molecule Dataset |
Name |
Description |
Type |
---|---|---|
Response Value Field |
Name of the field containing the primary data being trained on and predicted. |
Float |
Custom Feature
|
Field containing feature vector to train model on. Must be a float vector
|
FloatVec |
Number of Models to show in Floe report |
How many best models to provide in FloeReport. By default, keeps best
20 models (based on r2 score) such that it meet memory requirement
|
Int |
Preprocess Molecule |
Preprocess by Neutral Ph, Largest Mol, Blockbuster Filter |
Bool |
Apply Blockbuster filter |
For every molecule, stores only largest component, adjusts ionization to Neutral Ph |
Bool |
Negative Log |
Transform Learning Value to Negative Log.
Only for Regression. False: Build Tensorflow Neural Network Model
for prediction and explanation (Deterministic Model)
|
Bool |
Number of Top features to explain |
Number of top features to provide LIME votes results for.
These are the most important features that determine property prediction based on trained ML Model
|
List |
Name |
Description |
Type |
---|---|---|
Dropouts |
List of dropout hyperparameters. |
FloatVec |
Sets of Hidden Layers |
list(s) of hidden layers separated by -1. Input and output layer will be determined by data.
Eg: 150,100,50 will create NN with 3 hidden layers of size 150, 100, 50.
|
IntVec |
Sets of Regularisation Layers |
list(s) of regularisation layers separated by -1.
No regularisation on Input and output layer.
|
FloatVec |
Learning Rates |
List of all the learning rate hyperparameters to train model. |
FloatVec |
Max Epochs |
Maximum number of epochs to train model. |
Int |
Activation |
Activation Functions: ReLU, LeakyReLU, PReLU, tanh, SELU, ELU |
List |
Batch Size |
Batch size for training regressor |
Int |
Name |
Description |
Type |
---|---|---|
Models Built |
Output of Generated Models |
Dataset |
Failure Output |
Output of Failure |
Dataset |