What is bagging fraction in light GBM?

What is bagging fraction in light GBM?

feature_fraction. Feature fraction or sub_feature deals with column sampling, LightGBM will randomly select a subset of features on each iteration (tree). For example, if you set it to 0.6, LightGBM will select 60% of features before training each tree.

What is lambda in L2 regularization?

In L2, we have: Here, lambda is the regularization parameter. It is the hyperparameter whose value is optimized for better results. L2 regularization is also known as weight decay as it forces the weights to decay towards zero (but not exactly zero).

Can you use both L1 and L2 regularization?

Regularization Term Both L1 and L2 can add a penalty to the cost depending upon the model complexity, so at the place of computing the cost by using a loss function, there will be an auxiliary component, known as regularization terms, added in order to panelizing complex models.

How can I improve my LightGBM accuracy?

For better accuracy:

1. Use large max_bin (may be slower)
2. Use small learning_rate with large num_iterations.
3. Use large num_leaves (may cause over-fitting)
4. Use bigger training data.
5. Try dart.
6. Try to use categorical feature directly.

Is L2 regularization better than L1?

From a practical standpoint, L1 tends to shrink coefficients to zero whereas L2 tends to shrink coefficients evenly. L1 is therefore useful for feature selection, as we can drop any variables associated with coefficients that go to zero. L2, on the other hand, is useful when you have collinear/codependent features.

Which is the L1 regularization term in LightGBM?

– L1 regularization term on weights. reg_lambda ( float, optional (default=0.)) – L2 regularization term on weights. random_state ( int, RandomState object or None, optional (default=None)) – Random number seed. If int, this number is used to seed the C++ code.

How are L1 and L2 regularization parameters related?

Summarized briefly here: These lightGBM L1 and L2 regularization parameters are related leaf scores, not feature weights. The regularization terms will reduce the complexity of a model (similar to most regularization efforts) but they are not directly related to the relative weighting of features.

Are there positive L1 and L2 params in lgbm model?

While reading about tuning LGBM parameters I cam across one such case: Kaggle official GBDT Specification and Optimization Workshop in Paris where Instructors are ML experts. And these experts have used positive values of both L1 & L2 params in LGBM model. Link below (Ctrl+F ‘search_spaces’ to directly reach parameter grid in this long kernel)

What are the parameters of a dataset in LightGBM?

Dataset Parameters ¶ 1 max number of bins that feature values will be bucketed in 2 small number of bins may reduce training accuracy but may increase general power (deal with over-fitting) 3 LightGBM will auto compress memory according to max_bin. For example, LightGBM will use uint8_t for feature value if max_bin=255