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Decision forests are most effective when you have a tabular dataset (data you
might represent in a spreadsheet, csv file, or database table). Tabular data is
one of the most common data formats, and decision forests should be your “go-to”
solution for modeling it.
Table 1. An example of a tabular dataset.
Number of legs
Number of eyes
Weight (lbs)
Species (label)
2
2
12
Penguin
8
6
0.1
Spider
4
2
44
Dog
…
…
…
…
Unlike neural networks, decision forests natively consume model tabular data.
When developing decision forests, you don't have to do tasks like the following:
Perform preprocessing like feature normalization or one-hot encoding.
Perform imputation (for example, replacing a missing value with -1).
However, decision forests are not well suited to directly consume non-tabular
data (also called unstructured data), such as images or text. Yes, workarounds
for this limitation do exist, but neural networks generally handle unstructured
data better.
Performance
Decision forests are sample efficient. That is, decision forests are well suited
for training on small datasets, or on datasets where the ratio of number of
features / number of examples is high (possibly greater than 1). Even though
decision forests are sample efficient, like all machine learning models,
decision forests perform best when lots of data is available.
Decision forests typically infer faster than comparable neural
networks. For example, a medium-size decision forest runs inference in a few
microseconds on a modern CPU.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-08-25 UTC."],[[["\u003cp\u003eDecision forests are highly effective for modeling tabular data, making them a primary choice for datasets commonly found in spreadsheets, CSV files, or databases.\u003c/p\u003e\n"],["\u003cp\u003eUnlike neural networks, decision forests directly handle tabular data without requiring preprocessing steps like feature normalization or imputation.\u003c/p\u003e\n"],["\u003cp\u003eWhile decision forests can be adapted for non-tabular data like images or text, neural networks are generally better suited for such data types.\u003c/p\u003e\n"],["\u003cp\u003eDecision forests are sample efficient, performing well even with small datasets or those with a high feature-to-example ratio, but still benefit from larger datasets.\u003c/p\u003e\n"],["\u003cp\u003eDecision forests offer faster inference speeds compared to neural networks, typically completing predictions within microseconds on modern CPUs.\u003c/p\u003e\n"]]],[],null,["# Appropriate data for decision forests\n\n\u003cbr /\u003e\n\nDecision forests are most effective when you have a tabular dataset (data you\nmight represent in a spreadsheet, csv file, or database table). Tabular data is\none of the most common data formats, and decision forests should be your \"go-to\"\nsolution for modeling it.\n\n**Table 1. An example of a tabular dataset.**\n\n| Number of legs | Number of eyes | Weight (lbs) | Species (label) |\n|----------------|----------------|--------------|-----------------|\n| 2 | 2 | 12 | Penguin |\n| 8 | 6 | 0.1 | Spider |\n| 4 | 2 | 44 | Dog |\n| ... | ... | ... | ... |\n\nUnlike neural networks, decision forests natively consume model tabular data.\nWhen developing decision forests, you don't have to do tasks like the following:\n\n- Perform preprocessing like feature normalization or one-hot encoding.\n- Perform imputation (for example, replacing a missing value with `-1`).\n\nHowever, decision forests are not well suited to directly consume non-tabular\ndata (also called unstructured data), such as images or text. Yes, workarounds\nfor this limitation do exist, but neural networks generally handle unstructured\ndata better.\n\nPerformance\n-----------\n\nDecision forests are sample efficient. That is, decision forests are well suited\nfor training on small datasets, or on datasets where the ratio of number of\nfeatures / number of examples is high (possibly greater than 1). Even though\ndecision forests are sample efficient, like all machine learning models,\ndecision forests perform best when lots of data is available.\n\nDecision forests typically infer faster than comparable neural\nnetworks. For example, a medium-size decision forest runs inference in a few\nmicroseconds on a modern CPU."]]