Can decision trees handle continuous variables?
The splitting is done based on the normalized information gain and the feature having the highest information gain makes the decision. Unlike ID3, it can handle both continuous and discrete attributes very efficiently and after building a tree, it undergoes pruning by removing all the branches having low importance.
Does missing values affect decision tree?
Missing attribute values are a common occurrence in data, either through errors made when the values were recorded or because they were judged irrelevant to the particular case. Such lacunae affect both the way that a decision tree is constructed and its use to classify a new case.
Can chaid handle missing values?
CHAID and Exhaustive CHAID treat all system- and user-missing values for each independent variable as a single category. For cases in which the value for that variable is missing, other independent variables having high associations with the original variable are used for classification.
How does the decision tree algorithm work with missing values?
It looks like the decision tree algorithm works quite nicely with missing attributes, however when I try to apply the model on the training data set the results are much worse. Is the apply model operator capable of classifying samples with missing values, or is the decision tree algorithm just unrealisticly optimistic with missing values.
Are there any machine learning algorithms that ignore missing values?
Using Algorithms that support missing values: All the machine learning algorithms don’t support missing values but some ML algorithms are robust to missing values in the dataset. The k-NN algorithm can ignore a column from a distance measure when a value is missing. Naive Bayes can also support missing values when making a prediction.
How are missing values handled in a split?
The real handling approaches to missing data does not use data point with missing values in the evaluation of a split. However, when child nodes are created and trained, those instances are distributed somehow. I know about the following approaches to distribute the missing value instances to child nodes:
How to delete rows with missing values in machine learning?
Delete Rows with Missing Values: Missing values can be handled by deletin g the rows or columns having null values. If columns have more than half of rows as null then the entire column can be dropped. The rows which are having one or more columns values as null can also be dropped.
https://www.youtube.com/watch?v=s83vhIcdLPc