Hyperparameters tuning

Hyperparameters tuning

Don’t spend too much time tuning hyperparameters, Only if you don’t have any more ideas or you have spare computational resources

General pipeline

  1. Select the most influential parameters
  • There are tons of parameters and we can’t tune all of them
  1. Understand, how exactly they influence the training
  • A parameter in red
    – Increase it to underfitting
    – Increasing it impedes fitting
    – Decrease to allow model fit easier
  • A parameter in green
    • Increasing it leads to a better fit(overfit) on trainset
    • Increase it,if model underfits
    • Decreaseifoverfits
  1. Tune them!
  • Manually (change and examine)
  • Automatically(hyperopt,etc.)
    – Hyperopt
    – Scikit-optimize
    – Spearmint
    – GPyOpt
    – RoBO
    – SMAC3

Tree based model

GBDT

XGBoost LightGBM
max_depth max_depth/num_leaves
subsample bagging_fraction
colsample_bytree/colsample_bylevel feature_fraction
min_child_weight min_data_in_leaf
lambda, alpha lambda, alpha
eta learning_rate
num_round num_iterations
seed seed

RandomForest/ExtraTrees

  • N_estimators (the higher the better)
  • cmax_depth
  • cmax_features
  • cmin_samples_leaf
  • criterion (‘gini’ is better in most of time)
  • random_state
  • n_jobs

Neural Nets

  • Number of neurons per layer
  • Number of layers
  • Optimizers
    SGD + momentum
    Adam/Adadelta/Adagrad/…
      - In practice lead to more overfitting
    
  • Batch size
  • Learning rate (not too high or not too low, depend on other parameters)
  • Regularization
    L2/L1 for weights
    Dropout/Dropconnect
    Static dropconnect

Linear Model

Regularization parameter (C, alpha, lambda, …)
– Start with very small value and increase it.
– SVC starts to work slower as C increases

Regularization type
– L1/L2/L1+L2 — try each
– L1 can be used for feature selection

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