Beer Rating Predictor
Model Selection
Model       Choose the ML model to train.
Random Forest Parameters
n_estimators Number of trees.
max_depth Max depth of trees.
min_samples_leaf Min samples per leaf.
SVM Parameters
C Regularization parameter.
kernel   Kernel type.
gamma   Gamma setting.
XGBoost Parameters
n_estimators Number of boosting rounds.
learning_rate Learning rate.
max_depth Max tree depth.
Neural Network Parameters
hidden_units Neurons per hidden layer.
layers Number of hidden layers (0 = linear model).
activation   Activation function.
learning_rate Adam learning rate.
l2 (weight decay) L2 regularization (Adam weight_decay).
dropout Dropout rate (0 = no dropout).
batch_size Mini-batch size.
  

Predicts beer overall rating using ML models trained on the Beer Profile and Ratings dataset. Select a model, adjust its parameters, and click Train.

Training the model...

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