{"id":11500,"date":"2020-07-18T16:09:18","date_gmt":"2020-07-18T16:09:18","guid":{"rendered":"https:\/\/holypython.com\/?page_id=11500"},"modified":"2023-03-25T13:06:29","modified_gmt":"2023-03-25T13:06:29","slug":"random-forest-pros-cons","status":"publish","type":"page","link":"https:\/\/holypython.com\/rf\/random-forest-pros-cons\/","title":{"rendered":"Random Forest Pros & Cons"},"content":{"rendered":"\t\t
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Random Forest<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Pros & Cons<\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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random forest<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Advantages<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t1- Excellent Predictive Powers\t\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/h4>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t

\n\t\t\t\t\t\tIf you like Decision Trees, Random Forests are like decision trees on 'roids. <\/i>

Being consisted of multiple decision trees amplifies random forest's predictive capabilities and makes it useful for application where accuracy really matters.<\/i>\t\t\t\t\t<\/p>\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t2- No Normalization\t\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/h4>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t

\n\t\t\t\t\t\tRandom Forests also don't require normalization<\/b><\/i>\t\t\t\t\t<\/p>\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

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\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t3- Easy Data Preperation\t\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/h4>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t

\n\t\t\t\t\t\tOverall no requirements for normalization or scaling makes Random Forests much more convenient than some other machine learning algorithms when it comes to \"data preperation and pre-processing\".<\/i>\t\t\t\t\t<\/p>\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t4- Missing Data\t\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/h4>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t

\n\t\t\t\t\t\tJust like decision trees, random forests handle missing values like a champ which brings us back to point: \"Easy Data Preperation<\/b>\"<\/i>

With random forests you can also expect more accurate results since they average results of multiple decision trees in a random but specific way.<\/i>\t\t\t\t\t<\/p>\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

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\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t5- Fast Training (may be wrong)\t\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/h4>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t

\n\t\t\t\t\t\tTraining process is relatively faster for decision trees<\/b> compared to some other algorithms such as random forests.

This makes sense since random forest<\/b> deals with multiple trees and decision tree<\/b> is concerned with a single decision tree.<\/i>\t\t\t\t\t<\/p>\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t6- Optimization Options\t\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/h4>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t

\n\t\t\t\t\t\tThis can be a blessing and a curse depending on what you want. <\/i>

But random forest offers lots of parameters to tweak and improve your machine learning model.<\/i>

Just for computational efficiency, oob_score, n_estimators, random_state, warm_start and n_jobs are just a few that comes to mind.<\/i>\t\t\t\t\t<\/p>\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

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\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t7- Suitable for large data\t\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/h4>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t

\n\t\t\t\t\t\tIf you have a large database or a huge dataset you need to work on, no worries. Usually, random forest algorithm handles large datasets pretty well.<\/i>

Furthermore, you can take advantage of optimization parameters and make your model more efficient than it would be out of the box.<\/i>\t\t\t\t\t<\/p>\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t8- Sophisticated Output\t\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/h4>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t

\n\t\t\t\t\t\tRandom Forest also provides a very nice and sophisticated output with variable importance.<\/i>

This interpretation can help you go beyond an accurate prediction and comment on what's more important in achieving the prediction and why.<\/i>\t\t\t\t\t<\/p>\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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\n\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\tmachine learning\t\t\t\t<\/span>\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Holy Python is reader-supported. When you buy through links on our site, we may earn an affiliate commission.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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random forest<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Disadvantages<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t1- Overfitting Risk\t\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/h4>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t

\n\t\t\t\t\t\tAlthough much lower than decision trees, overfitting is still a risk with random forests and something you should monitor.<\/i>\t\t\t\t\t<\/p>\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t2- Limited with Regression\t\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/h4>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t

\n\t\t\t\t\t\tRandom Forest regression is a thing, however, with so many regression model opportunities out there in data science world, random forests may not be the go-to regression approach in every application.<\/i>

It will not be able to predict any value outside the available values since averaging is a big part of random forest models.<\/i>

So, while random forest is almost unmatched in most classification solutions, it can be limited with regression especially when data has linear nature.<\/i>\t\t\t\t\t<\/p>\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

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\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t3- Parameter Complexity\t\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/h4>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t

\n\t\t\t\t\t\tParameter complexity (used for tuning and optimization) doesn't really compare to a task like manually writing a random forest algorithm let alone discovering it.<\/i>

However, some algorithms are still more complex than others when it comes to optimization and parameters. Although random forests are known to work well without too much optimization they still have lots of hyper-parameters that can -and in some cases should- be adjusted.<\/i>

On top of decision tree parameters (such as leaf, node, splitting, tree size etc.) random forests also have parameters regarding tree amount in the forest (n_parameters<\/b>), tree building methods (bootstrap<\/b>) and out of bag score (oob_score<\/b>)<\/i>\t\t\t\t\t<\/p>\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t4- Biased towards variables with more levels\t\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/h4>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t

\n\t\t\t\t\t\tIf your data has categorical variables with different levels of attributes this can be a big problem because random forest algorithm will favor those with more values which can pose a prediction risk.<\/i>\t\t\t\t\t<\/p>\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

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\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t5- TradeMark situation\t\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/h4>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t

\n\t\t\t\t\t\tIf you are going to use random forests in business or a commercial application, you totally can.<\/i>

But, know that you can't use the name Random Forest and many of its variations as your product or a part of your product without official permission from the owner entities of the trademark.<\/i>

Random Forests are a bit unique in this sense however, it's still shared under a generous license and this is just an interesting anecdote rather than a significant bottleneck.<\/i>

I've never heard anyone getting in legal trouble for using the random forest name inappropriately but you still want to respect the agreements and will of the inventors.<\/i>

Nevertheless, I've seen\/heard some people unnecessarily avoid random forests because of this point because either it's a turn off for them or they don't understand the difference between TM and patent.<\/i>\t\t\t\t\t<\/p>\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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wrap-up<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Random Forest Pros & Cons Summary<\/span><\/h3><\/div>\r\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Why Random Forests?<\/i><\/span><\/h2><\/div>\r\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Random Forest technology takes decision trees combines them in a sophisticated way and bring the whole idea to another level that's applicable to real, modern day problems.<\/i>

With so much great power, prediction accuracy, insightful output, ability to navigate with large datasets or databases and ability to handle missing data so well, no wonder why random forests are one of the most commonly utilized and popular machine learning algorithms out there.<\/i>

It makes one appreciate that founders Leo Breiman and Adele Cutler only TradeMarked the technology, not patent it, and shared it under a license that's free for all to use. Oh, you never heard of the:<\/i>
\"interesting history of Random Forests<\/a>\", well, why don't you check it out?<\/i><\/div>\n\t\t\t\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tGreat Accuracy\t\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/h4>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t

\n\t\t\t\t\t\tFirst thing that comes to mind with Random Forests is their prediction accuracy.<\/i>\t\t\t\t\t<\/p>\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tUnlikely to overfit\t\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/h4>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t

\n\t\t\t\t\t\tSince averaging of decision trees takes place you can expect less overfitting and less getting stuck with local minima.<\/i>\t\t\t\t\t<\/p>\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tEasy Data Prep\t\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/h4>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t

\n\t\t\t\t\t\tData prep is no big deal. No scaling, no normalization, missing data is OK. <\/i>\t\t\t\t\t<\/p>\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tGreat Reports\t\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/h4>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t

\n\t\t\t\t\t\tRandom Forests give out great reports with variable importance information. <\/i>\t\t\t\t\t<\/p>\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tParameter Complexity\t\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/h4>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t

\n\t\t\t\t\t\tOn top of decision trees' parameters (think nodes, leaves, splitting, tree size) you can expect to have more parameters with random forests regarding tree amount etc.<\/i>\t\t\t\t\t<\/p>\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tRelatively Slow\t\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/h4>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t

\n\t\t\t\t\t\tRandom Forests can get sluggish especially if your grow your forest with too many trees and not optimize well.<\/i>\t\t\t\t\t<\/p>\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tLimited Regression\t\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/h4>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t

\n\t\t\t\t\t\tDon't let random forests' superpowers trick you they can perform pretty badly in specific regression problems. (Linear data)<\/i>\t\t\t\t\t<\/p>\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

Random Forest Pros & Cons random forest Advantages 1- Excellent Predictive Powers If you like Decision Trees, Random Forests are like decision trees on ‘roids. Being consisted of multiple decision trees amplifies random forest’s predictive capabilities and makes it useful for application where accuracy really matters. 2- No Normalization Random Forests also don’t require normalization […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":11460,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":""},"class_list":["post-11500","page","type-page","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/holypython.com\/wp-json\/wp\/v2\/pages\/11500","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/holypython.com\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/holypython.com\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/holypython.com\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/holypython.com\/wp-json\/wp\/v2\/comments?post=11500"}],"version-history":[{"count":0,"href":"https:\/\/holypython.com\/wp-json\/wp\/v2\/pages\/11500\/revisions"}],"up":[{"embeddable":true,"href":"https:\/\/holypython.com\/wp-json\/wp\/v2\/pages\/11460"}],"wp:attachment":[{"href":"https:\/\/holypython.com\/wp-json\/wp\/v2\/media?parent=11500"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}