Is max_depth in scikit the equivalent of pruning in decision trees?

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I was analyzing the classifier created using a decision tree. There is a tuning parameter called max_depth in scikit's decision tree. Is this equivalent of pruning a decision tree? If not, how could I prune a decision tree using scikit?

dt_ap = tree.DecisionTreeClassifier(random_state=1, max_depth=13)
boosted_dt = AdaBoostClassifier(dt_ap, random_state=1)
boosted_dt.fit(X_train, Y_train)

machine-learning python scikit-learn decision-trees supervised-learning

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asked 5 hours ago

Suhail Gupta

1907

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I was analyzing the classifier created using a decision tree. There is a tuning parameter called max_depth in scikit's decision tree. Is this equivalent of pruning a decision tree? If not, how could I prune a decision tree using scikit?

dt_ap = tree.DecisionTreeClassifier(random_state=1, max_depth=13)
boosted_dt = AdaBoostClassifier(dt_ap, random_state=1)
boosted_dt.fit(X_train, Y_train)

machine-learning python scikit-learn decision-trees supervised-learning

share|improve this question

asked 5 hours ago

Suhail Gupta

1907

add a comment | 

up vote
2
down vote

favorite

up vote
2
down vote

favorite

I was analyzing the classifier created using a decision tree. There is a tuning parameter called max_depth in scikit's decision tree. Is this equivalent of pruning a decision tree? If not, how could I prune a decision tree using scikit?

dt_ap = tree.DecisionTreeClassifier(random_state=1, max_depth=13)
boosted_dt = AdaBoostClassifier(dt_ap, random_state=1)
boosted_dt.fit(X_train, Y_train)

machine-learning python scikit-learn decision-trees supervised-learning

share|improve this question

asked 5 hours ago

Suhail Gupta

1907

I was analyzing the classifier created using a decision tree. There is a tuning parameter called max_depth in scikit's decision tree. Is this equivalent of pruning a decision tree? If not, how could I prune a decision tree using scikit?

dt_ap = tree.DecisionTreeClassifier(random_state=1, max_depth=13)
boosted_dt = AdaBoostClassifier(dt_ap, random_state=1)
boosted_dt.fit(X_train, Y_train)

machine-learning python scikit-learn decision-trees supervised-learning

machine-learning python scikit-learn decision-trees supervised-learning

share|improve this question

asked 5 hours ago

Suhail Gupta

1907

share|improve this question

asked 5 hours ago

Suhail Gupta

1907

share|improve this question

share|improve this question

asked 5 hours ago

Suhail Gupta

1907

asked 5 hours ago

Suhail Gupta

1907

asked 5 hours ago

Suhail Gupta

1907

1907

add a comment | 

add a comment | 

1 Answer
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Is this equivalent of pruning a decision tree?

Though they have similar goals (i.e. placing some restrictions to the model so that it doesn't grow very complex and overfit), max_depth isn't equivalent to pruning. The way pruning usually works is that go back through the tree and replace branches that do not help with leaf nodes.

If not, how could I prune a decision tree using scikit?

You can't through scikit-learn (without altering the source code).

Quote taken from the Decision Tree documentation: Mechanisms such as pruning (not currently supported)

If you want to post-prune a tree you have to do it on your own:

You can read this excellent post detailing how to do so.

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answered 51 mins ago

Djib2011

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1 Answer
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1 Answer
1

active

oldest

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active

oldest

votes

up vote
2
down vote

Is this equivalent of pruning a decision tree?

Though they have similar goals (i.e. placing some restrictions to the model so that it doesn't grow very complex and overfit), max_depth isn't equivalent to pruning. The way pruning usually works is that go back through the tree and replace branches that do not help with leaf nodes.

If not, how could I prune a decision tree using scikit?

You can't through scikit-learn (without altering the source code).

Quote taken from the Decision Tree documentation: Mechanisms such as pruning (not currently supported)

If you want to post-prune a tree you have to do it on your own:

You can read this excellent post detailing how to do so.

share|improve this answer

answered 51 mins ago

Djib2011

1,742516

add a comment | 

up vote
2
down vote

Is this equivalent of pruning a decision tree?

Though they have similar goals (i.e. placing some restrictions to the model so that it doesn't grow very complex and overfit), max_depth isn't equivalent to pruning. The way pruning usually works is that go back through the tree and replace branches that do not help with leaf nodes.

If not, how could I prune a decision tree using scikit?

You can't through scikit-learn (without altering the source code).

Quote taken from the Decision Tree documentation: Mechanisms such as pruning (not currently supported)

If you want to post-prune a tree you have to do it on your own:

You can read this excellent post detailing how to do so.

share|improve this answer

answered 51 mins ago

Djib2011

1,742516

add a comment | 

up vote
2
down vote

up vote
2
down vote

Is this equivalent of pruning a decision tree?

Though they have similar goals (i.e. placing some restrictions to the model so that it doesn't grow very complex and overfit), max_depth isn't equivalent to pruning. The way pruning usually works is that go back through the tree and replace branches that do not help with leaf nodes.

If not, how could I prune a decision tree using scikit?

You can't through scikit-learn (without altering the source code).

Quote taken from the Decision Tree documentation: Mechanisms such as pruning (not currently supported)

If you want to post-prune a tree you have to do it on your own:

You can read this excellent post detailing how to do so.

share|improve this answer

answered 51 mins ago

Djib2011

1,742516

Is this equivalent of pruning a decision tree?

Though they have similar goals (i.e. placing some restrictions to the model so that it doesn't grow very complex and overfit), max_depth isn't equivalent to pruning. The way pruning usually works is that go back through the tree and replace branches that do not help with leaf nodes.

If not, how could I prune a decision tree using scikit?

You can't through scikit-learn (without altering the source code).

Quote taken from the Decision Tree documentation: Mechanisms such as pruning (not currently supported)

If you want to post-prune a tree you have to do it on your own:

You can read this excellent post detailing how to do so.

share|improve this answer

answered 51 mins ago

Djib2011

1,742516

share|improve this answer

share|improve this answer

answered 51 mins ago

Djib2011

1,742516

answered 51 mins ago

Djib2011

1,742516

answered 51 mins ago

Djib2011

1,742516

1,742516

add a comment | 

add a comment | 

 

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