I need some help in understanding the difference between regression trees and linear model tree.
Regards
Shahzad
A linear model tree is a decision tree with a linear functional model in each leaf, whereas in classical regression tree (e.g., CART) it is the sample mean of the response variable for statistical units in each leaf (hence, a constant) that is being considered. Linear model trees can be seen as a a form of locally weighted regression, while regression tree are piecewise-constant regression.
For more information on linear model trees, you can consult
Torgo, L. Functional models for regression tree
leaves.
In Proceedings of the 14th International Conference on Machine
Learning, pp. 385–393. Morgan Kaufmann, 1997.
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I am not very experienced with unsupervised learning, but my general understanding is that in unsupervised learning, the model learns without there being an output. However, during pre-training in models such as BERT or GPT-3, it seems to me that there is an output. For example, in BERT, some of the tokens in the input sequence are masked. Then, the model will try to predict those words. Since we already know what those masked words originally were, we can compare that with the prediction to find the loss. Isn't this basically supervised learning?
How does XGboost is performing the regression tasks?
Ex- We know that for a classification problem in Boosting, it is punishing the mis-classified points & the weightage is given more for them in the next stump.
How does the weightage is given in case of regression?
1.let’s go through a simple regression example, using Decision Trees as the base predictors (of course, Gradient Boosting also works great with regression tasks). This is called Gradient Tree Boosting, or Gradient Boosted Regression Trees (GBRT).
2.First, let’s fit a DecisionTreeRegressor to the training set (the ouput is a noise quadratic fit)
3.next we will train the second regression tree on the residuals made by the first regression tree.
4.Then we train a third regressor on the residual errors made by the second predictor
5.Now we have an ensemble containing three trees. It can make predictions on a new instance simply by adding up the predictions of all the trees:
I am learning the Random Forest Regression Model. I know that it forms many Trees(models) and then we can predict our target variables by averaging the result of all Trees. I also have a descent understanding of Decision Tree Regression Algorithm. How can we form the best number of Trees?
For example i have a dataset where i am predicting person salary and i have only two input variables that are 'Years of Experience', 'Performance Score ' then how many random Trees can i form using such dataset? Are Random Forest Trees dependent upon the number of input variables? Any Good Example will highly be appreciated..
Thanks in Advance
A decision tree trains the model on the entire dataset and only one model is created. In random forest, multiple decision trees are created and each decision tree is trained on a subset of data by limiting the number of rows and the features. In your case, you only have two features so the model will create and train data on subset of data.
You can create any number of random trees for your data. Usually in random forest, more trees result in better performance but also more computation time. Experiment with your data and see the performance changes between different number of trees. If performance remains same, then use less trees to have faster computation. You can use grid search for this.
Also you can experiment with other ml models like linear regression, which migh† perform well in your case.
I am a beginner in machine learning field and I want to learn how to do multiclass classification with Gradient Boosting Tree (GBT). I have read some of the articles about GBT but for regression problem and I couldn't find the right explanation about GBT for multiclass classfication. I also check GBT in scikit-learn library for machine learning. The implementation of GBT is GradientBoostingClassifier which used regression tree as the weak learners for multiclass classification.
GB builds an additive model in a forward stage-wise fashion; it allows for the optimization of arbitrary differentiable loss functions. In each stage n_classes_ regression trees are fit on the negative gradient of the binomial or multinomial deviance loss function. Binary classification is a special case where only a single regression tree is induced.
Source : http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#sklearn.ensemble.GradientBoostingClassifier
The things is, why do we use regression tree as our learners for GBT instead of classification tree ? It would be very helpful, if someone can provide me the explanation about why regression tree is being used rather than classification tree and how regression tree can do the classification. Thank you
You are interpreting 'regression' too literally here (as numeric prediction), which is not the case; remember, classification is handled with logistic regression. See, for example, the entry for loss in the documentation page you have linked:
loss : {‘deviance’, ‘exponential’}, optional (default=’deviance’)
loss function to be optimized. ‘deviance’ refers to deviance (= logistic regression) for classification with probabilistic outputs. For loss ‘exponential’ gradient boosting recovers the AdaBoost algorithm.
So, a 'classification tree' is just a regression tree with loss='deviance'...
What is the difference if we use Decision Tree as Base estimator in AdaBoost algorithm ?
Is Random Forest a special case of AdaBoost?
Most certainly not; Random Forest is a case of bagging ensemble algorithm (short for bootstrap aggregating), which is different from boosting - check here for their differences.
What is the difference if we use Decision Tree as Base estimator in AdaBoost algorithm ?
You don't get a Random Forest, but a Gradient Tree Boosting Machine, available in several packages like xgboost (R/Python), gbm (R), scikit-learn (Python) etc.
Check chapter 8 of the excellent (and freely available) book An Introduction to Statistical Learning for more, or The Elements of Statistical Learning (heavy in math & theory, not for the faint-hearted)...