# A practical approach to Tree Pruning using sklearn | Decision Trees

Updated on: April 5, 2020 · 9 mins read
Categories: machinelearning   | sklearn   | python   | datascience |

As we have already discussed in the regression tree post that a simple tree prediction can lead to a model which overfits the data and produce bad results with the test data. Tree Pruning is the way to reduce overfitting by creating smaller trees.

Tree Pruning isn’t only used for regression trees. We also make use of it in the classification trees as well.

As the word itself suggests, the process involves cutting the tree into smaller parts.

We can do pruning in two ways.

## Pre-pruning or early stopping

This means stopping before the full tree is even created. The idea is to build the tree only as long as the decrease in the RSS due to each split exceeds some threshold.

This means that we can stop further creation of the tree as soon as the RSS decrease while producing the next node is lower than the given threshold.

This might lead to some shortsightedness as there might be some cases in which there might a large reduction in the RSS at the later ends of the tree creation.

Thus, we try to make use of much more complex post pruning.

## Post Pruning

In Post pruning, we grow a large tree T0 and then prune it back in order to obtain a subtree such that we get the lowest test error rate.

Now, the problem with this algorithm is that we don’t want to go to every subtree and choose each one of them to calculate the change in the test error rate.

Cost complexity Pruning or Weakest link Pruning helps us with that. It introduces a new term, alpha. We pick only those trees which are indexed by this alpha.

$\alpha = alpha\$
For each value of alpha we have a subtree which can minimize the value of

$\displaystyle \sum_{m=1}^{\left | T \right |} \sum_{i:x_i \epsilon R_m} (y_i - \widehat{y}_{R_m})^ {2} + \alpha\left | T \right |$
$R_m\ is\ the\ rectangle\ corresponding\ to\ mth\ terminal\ node$
$\widehat{y}_{R_m}\ is\ the\ mean\ of\ training\ observations\ in\ R_m$

## Steps involved in building Regression Tree using Tree Pruning

• Split the data to grow the large tree stopping only when the terminal node contains fewer than some minimum number of observations. For example, we will keep dividing until each region has less than 20 data points.

• Apply cost complexity pruning to the large tree and get the sequence of best subtrees as a function of alpha. The idea is to minimize the cost-complexity function.

$C_\alpha(T) = R(T) + \alpha \left | T \right |$
$where\ T\ is\ the\ number\ of\ leaves\ of\ the\ tree$
$and\ R(T)\ is\ the\ loss\ function\ calculated\ across\ the\ leaves.$
• Use K-Fold cross-validation to choose alpha. Simply putting, divide the training data into K smaller parts and run the fit function on K iterations with leaving K different subsets of training data as validation data and finally calculate the Mean Square Error using these K validation subsets as a function of alpha. (Will discuss k-fold cross-validation in feature engineering post.)

• Pick the alpha value with a minimum average error.

• Return the subtree that corresponds to the chosen value of alpha.

## Using sklearn to see pruning effect on trees

We will use simple data to check the effect of pruning on the Decision Tree. Let’s first get the data and split it accordingly.

import matplotlib.pyplot as plt

from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier

X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)

To get the different applicable alpha values and corresponding impurity in the leaf nodes introduced due to that, we can use the cost_complexity_pruning_path function present in the DecisionTreeClassifier class.

clf = DecisionTreeClassifier()
path = clf.cost_complexity_pruning_path(X_train, y_train)
path

This gives,

{'ccp_alphas': array([0.        , 0.00226647, 0.00464743, 0.0046598 , 0.0056338 ,
0.00704225, 0.00784194, 0.00911402, 0.01144366, 0.018988  ,
0.02314163, 0.03422475, 0.32729844]),
'impurities': array([0.        , 0.00453294, 0.01847522, 0.02313502, 0.02876883,
0.03581108, 0.04365302, 0.05276704, 0.0642107 , 0.0831987 ,
0.10634033, 0.14056508, 0.46786352])}

This contains two Numpy Arrays of alpha and impurities.

We can plot this on a graph to see the relation.

ccp_alphas, impurities = path.ccp_alphas, path.impurities

plt.figure(figsize=(10, 6))
plt.plot(ccp_alphas, impurities)
plt.xlabel("effective alpha")
plt.ylabel("total impurity of leaves")

Finding an optimal value of alpha using Python

clfs = []

for ccp_alpha in ccp_alphas:
clf = DecisionTreeClassifier(random_state=0, ccp_alpha=ccp_alpha)
clf.fit(X_train, y_train)
clfs.append(clf)

As we already know that there is a strong relation between, alpha and the depth of the tree. We can find the relation using this plot.

tree_depths = [clf.tree_.max_depth for clf in clfs]
plt.figure(figsize=(10,  6))
plt.plot(ccp_alphas[:-1], tree_depths[:-1])
plt.xlabel("effective alpha")
plt.ylabel("total depth")

Use the following code to find the relation between alpha and accuracy.

from sklearn.metrics import accuracy_score

acc_scores = [accuracy_score(y_test, clf.predict(X_test)) for clf in clfs]

tree_depths = [clf.tree_.max_depth for clf in clfs]
plt.figure(figsize=(10,  6))
plt.grid()
plt.plot(ccp_alphas[:-1], acc_scores[:-1])
plt.xlabel("effective alpha")
plt.ylabel("Accuracy scores")

We can clearly see that somewhere around 0.013 alpha, we get a very good value of accuracy.

Here is the notebook for the following code.