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What is instance weighting?
Instance weights define the capacity units that each instance type would contribute to your application’s performance, providing greater flexibility for instance type selection that can be included in your ASG. You can also use the dynamic and predictive scaling features to add or remove EC2 instances.
Why does adaboost increase the weights of the misclassified points?
This is done by making misclassified cases to be updated with increased weights after an iteration. Increased weights would make our learning algorithm pay higher attention to these observations in the next iteration.
What is weighted capacity AWS?
When you configure an Auto Scaling group to launch multiple instance types, you have the option of defining the number of capacity units that each instance contributes to the desired capacity of the group, using instance weighting. By default, all instance types are treated as the same weight.
What is a spot block in AWS?
Spot blocks allow you to request Amazon EC2 Spot instances for 1 to 6 hours at a time to avoid being interrupted while your job completes. Previously, you could only request a single instance type at a time, and determining the most cost effective instances was a manual process.
How do weights affect the performance of AdaBoost?
In each iteration, AdaBoost identifies miss-classified data points, increasing their weights (and decrease the weights of correct points, in a sense) so that the next classifier will pay extra attention to get them right.The following figure illustrates how weights impact the performance of a simple decision stump (tree with depth 1)
Which is the best method for boosting models?
Here I’ll introduce the most popular method called SAMME, a specific method that deals with multi-classification problems. ( Zhu, H. Zou, S. Rosset, T. Hastie, “Multi-class AdaBoost”, 2009 ). AdaBoost trains a sequence of models with augmented sample weights, generating ‘confidence’ coefficients Alpha for individual classifiers based on errors.
Which is base estimator is used for boosting?
The base estimator from which the boosted ensemble is built. If None, then the base estimator is DecisionTreeClassifier (max_depth=1) The maximum number of estimators at which boosting is terminated. In case of perfect fit, the learning procedure is stopped early. learning_rate : float, optional (default=1.)
How to calculate Min child weight in XGBoost?
Explanation of min_child_weight in xgboost algorithm. minimum sum of instance weight (hessian) needed in a child. If the tree partition step results in a leaf node with the sum of instance weight less than min_child_weight, then the building process will give up further partitioning.