How Hyper parameter tuning is done?

How Hyper parameter tuning is done?

Hyperparameter tuning is choosing a set of optimal hyperparameters for a learning algorithm. A hyperparameter is a model argument whose value is set before the learning process begins. The key to machine learning algorithms is hyperparameter tuning.

Why do we do hyper parameter tuning?

Often, we are not aware of optimal values for hyperparameters which would generate the best model output. So, what we tell the model is to explore and select the optimal model architecture automatically. This selection procedure for hyperparameter is known as Hyperparameter Tuning.

How do I tune my lightGBM parameters?

According to lightGBM documentation, when facing overfitting you may want to do the following parameter tuning:

1. Use small max_bin.
2. Use small num_leaves.
3. Use min_data_in_leaf and min_sum_hessian_in_leaf.
4. Use bagging by set bagging_fraction and bagging_freq.
5. Use feature sub-sampling by set feature_fraction.

How to do hyper parameter tuning in XGBoost?

Thus, for practical reasons and to avoid the complexities involved in doing hybrid continuous-discrete optimization, most approaches to hyper-parameter tuning start off by discretizing the ranges of all hyper-parameters in question. For example, for our XGBoost experiments below we will fine-tune five hyperparameters.

Which is the only parameter set in xgbclassifier?

I am using XGBClassifier for building model and the only parameter I manually set is scale_pos_weight : 23.34 (0 value counts / 1 value counts) and it’s giving around 82% under AUC metric. I guess I can get much accuracy if I hypertune all other parameters.

Which is the best hyper Param tuning for Kagglers?

Due to the outstanding accuracy obtained by XGBoost, as well as its computational performance, it is perhaps the most popular choice among Kagglers and many other ML practitioners for purely “tabular” problems such as this one. Now, for each of the three hyper-param tuning methods mentioned above, we ran 10,000 independent trials.

What is the F1 score of XGBoost classifier?

Ok, Logistic Regression worked fine, nothing special, but still 0.62 f1 score on test dataset is pretty well, model also does not overfit. But we want better score, let’s try now SVM that should work well on low dimension data