**Nov 1, 2020 · Cost complexity pruning alpha is a parameter used for pruning trees. **

**Accuracy vs alpha for training and testing sets When ccp_alpha is set to zero and keeping the other default parameters of :class:DecisionTreeClassifier, the tree overfits, leading to a 100% training accuracy and 88% testing accuracy. **

**0. I understand that it seeks to find a sub-tree of the generated model that reduces overfitting, while using values of ccp_alpha determined by the. **

**My initial thought was that we have a set of. **

**As you can notice one of the values of k (which is actually the tuning parameter α for cost-complexity pruning) equals − ∞. **

**. The subtree with the largest cost complexity that is smaller than ccp_alpha will be chosen. 0, inf). **

**The complexity parameter is used to define the cost-complexity measure, \(R_\alpha(T)\) of a given tree \(T\): \[R_\alpha(T) = R(T) + \alpha|\widetilde{T}|\] where \(|\widetilde{T}|\) is the number of terminal. **

**. See Minimal Cost-Complexity Pruning for details. See Minimal Cost-Complexity Pruning for details. **

**I found that DecisionTree in sklearn has a function called cost_complexity_pruning_path, which gives the effective alphas of subtrees during pruning. That is divide the training observations into K fold. **

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**Pruning**by Cross-Validation.**fc-smoke">May 31, 2022 · STEP 1: Importing Necessary Libraries. **

**Lower ccp_ alpha’s indicate higher cost complexity. **

**Complexity** parameter used for Minimal **Cost**-**Complexity Pruning**. At step i {\displaystyle i} , the tree is created by removing a subtree from tree i − 1 {\displaystyle i-1} and replacing it with a leaf node with value chosen as in the tree. 2 - Minimal **Cost**-**Complexity Pruning**;.

**. . It provides another option to control the tree size. Let \(\ alpha ≥ 0\) be a real number called the complexity parameter and define the cost-complexity measure \(R_{\alpha}(T)\) as: \(R_{\alpha}(T)=R(T) +\alpha| \tilde{T}| \) The more leaf nodes that the tree contains the higher complexity of the tree because we have more flexibility in partitioning the space into smaller pieces, and therefore. STEP 3: Data Preprocessing (Scaling) STEP 4: Creation of Decision Tree Regressor model using training set. **

**Minimal Cost-Complexity Pruning Algorithm. **

**Unfortunately, sklearn does not have a tuning parameter (often referred to as alpha in other programming languages), but we can take care of tree pruning by tuning the max_depth parameter. Complexity parameter used for Minimal Cost-Complexity Pruning. **

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**See Minimal Cost-Complexity Pruning for details. **

**In python, sci-kit learn helps us implement cost complexity pruning using the parameter called ccp_alpha. **

**Unfortunately, sklearn does not have a tuning parameter (often referred to as alpha in other programming languages), but we can take care of tree pruning by tuning the max_depth parameter. **

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