- 1 When building a decision tree which feature should be selected for splitting?
- 2 What is the basis of splitting columns in decision trees?
- 3 What is first split in decision tree?
- 4 Can a decision tree have more than 2 splits?
- 5 How will you counter Overfitting in the decision tree?
- 6 How do you calculate information Split?
- 7 What are the disadvantages of decision tree?
- 8 Which node has maximum entropy in decision tree?
- 9 Is decision tree a regression?
- 10 What is the difference between decision tree and random forest?
- 11 What is a pure node in decision tree?
- 12 Is decision tree supervised or unsupervised?
- 13 Does a decision tree have to be binary?
- 14 What kind of data is best for decision tree?
- 15 Can decision trees only predict discrete outcomes?
When building a decision tree which feature should be selected for splitting?
The Gini Index The key to building a decision tree is determining the optimal split at each decision node. Using the simple example above, how did we know to split the root at a width (X1) of 5.3? The answer lies with the Gini index or score. The Gini index is a cost function used to evaluate splits.
What is the basis of splitting columns in decision trees?
In the decision tree chart, each internal node has a decision rule that splits the data. Gini referred to as the Gini ratio, which measures the impurity of the node. You can say a node is pure when all of its records belong to the same class, such nodes known as the leaf node.
What is first split in decision tree?
To build the tree, the information gain of each possible first split would need to be calculated. The best first split is the one that provides the most information gain. This process is repeated for each impure node until the tree is complete.
Can a decision tree have more than 2 splits?
The decision tree will never create more splits than the number of levels in the Y variable.
How will you counter Overfitting in the decision tree?
increased test set error. There are several approaches to avoiding overfitting in building decision trees. Pre-pruning that stop growing the tree earlier, before it perfectly classifies the training set. Post-pruning that allows the tree to perfectly classify the training set, and then post prune the tree.
How do you calculate information Split?
Information Gain is calculated for a split by subtracting the weighted entropies of each branch from the original entropy. When training a Decision Tree using these metrics, the best split is chosen by maximizing Information Gain.
What are the disadvantages of decision tree?
Disadvantages of decision trees: They are unstable, meaning that a small change in the data can lead to a large change in the structure of the optimal decision tree. They are often relatively inaccurate. Many other predictors perform better with similar data.
Which node has maximum entropy in decision tree?
Entropy is highest in the middle when the bubble is evenly split between positive and negative instances.
Is decision tree a regression?
Decision tree builds regression or classification models in the form of a tree structure. It breaks down a dataset into smaller and smaller subsets while at the same time an associated decision tree is incrementally developed. Decision trees can handle both categorical and numerical data.
What is the difference between decision tree and random forest?
A decision tree combines some decisions, whereas a random forest combines several decision trees. Thus, it is a long process, yet slow. Whereas, a decision tree is fast and operates easily on large data sets, especially the linear one. The random forest model needs rigorous training.
What is a pure node in decision tree?
The decision to split at each node is made according to the metric called purity. A node is 100% impure when a node is split evenly 50/50 and 100% pure when all of its data belongs to a single class.
Is decision tree supervised or unsupervised?
Decision Trees are a non-parametric supervised learning method used for both classification and regression tasks. Tree models where the target variable can take a discrete set of values are called classification trees.
Does a decision tree have to be binary?
For practical reasons (combinatorial explosion) most libraries implement decision trees with binary splits. The nice thing is that they are NP-complete (Hyafil, Laurent, and Ronald L. Rivest. “Constructing optimal binary decision trees is NP-complete.” Information Processing Letters 5.1 (1976): 15-17.)
What kind of data is best for decision tree?
- Decision trees are used for handling non-linear data sets effectively.
- The decision tree tool is used in real life in many areas, such as engineering, civil planning, law, and business.
- Decision trees can be divided into two types; categorical variable and continuous variable decision trees.
Can decision trees only predict discrete outcomes?
Decision trees belong to a class of supervised machine learning algorithms, which are used in both classification (predicts discrete outcome) and regression (predicts continuous numeric outcomes) predictive modeling.