{"id":3555,"date":"2019-05-15T10:39:56","date_gmt":"2019-05-15T14:39:56","guid":{"rendered":"https:\/\/ibkrcampus.com\/?p=3555"},"modified":"2023-03-29T16:56:16","modified_gmt":"2023-03-29T20:56:16","slug":"gini-index-for-decision-trees-part-iii","status":"publish","type":"post","link":"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/gini-index-for-decision-trees-part-iii\/","title":{"rendered":"Gini Index For Decision Trees – Part III"},"content":{"rendered":"\n

Calculation of Gini Index of Open Interest for Positive Past Trend<\/strong><\/p>\n\n\n\n

P(Open Interest=High): 2\/6<\/p>\n\n\n\n

P(Open Interest=Low): 4\/6<\/p>\n\n\n\n

If (Open Interest = High & Return = Up), probability = 2\/2<\/p>\n\n\n\n

If (Open Interest = High & Return = Down), probability = 0<\/p>\n\n\n\n

Gini index = 1 \u2013 (sq(2\/2) + sq(0)) = 0<\/p>\n\n\n\n

If (Open Interest = Low & Return = Up), probability = 2\/4<\/p>\n\n\n\n

If (Open Interest = Low & Return = Down), probability = 2\/4<\/p>\n\n\n\n

Gini index = 1 \u2013 (sq(0) + sq(2\/4)) = 0.50<\/p>\n\n\n\n

Weighted sum of the Gini Indices can be calculated as follows:<\/p>\n\n\n\n

Gini Index for Open Interest = (2\/6)*0 + (4\/6)*0.50 = 0.33<\/p>\n\n\n\n

Calculation of Gini Index for Trading Volume<\/strong><\/p>\n\n\n\n

P(Trading Volume=High): 4\/6<\/p>\n\n\n\n

P(Trading Volume=Low): 2\/6<\/p>\n\n\n\n

If (Trading Volume = High & Return = Up), probability = 4\/4<\/p>\n\n\n\n

If (Trading Volume = High & Return = Down), probability = 0<\/p>\n\n\n\n

Gini index = 1 \u2013 (sq(4\/4) + sq(0)) = 0<\/p>\n\n\n\n

If (Trading Volume = Low & Return = Up), probability = 0<\/p>\n\n\n\n

If (Trading Volume = Low & Return = Down), probability = 2\/2<\/p>\n\n\n\n

Gini index = 1 \u2013 (sq(0) + sq(2\/2)) = 0<\/p>\n\n\n\n

Weighted sum of the Gini Indices can be calculated as follows:<\/p>\n\n\n\n

Gini Index for Trading Volume = (4\/6)*0 + (2\/6)*0 = 0<\/p>\n\n\n\n

We will split the node further using the \u2018Trading Volume\u2019 feature, as it has the minimum Gini index.<\/p>\n\n\n\n

Conclusion<\/strong><\/p>\n\n\n\n

Gini Index, unlike information gain, isn\u2019t computationally intensive as it doesn\u2019t involve the logarithm function used to calculate entropy in information gain, which is why Gini Index is preferred over Information gain.<\/p>\n","protected":false},"excerpt":{"rendered":"

Calculation of Gini Index of Open Interest for Positive Past Trend<\/p>\n","protected":false},"author":93,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[339,343,349,338,350,341,344],"tags":[851,595,494],"contributors-categories":[13654],"class_list":{"0":"post-3555","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-data-science","7":"category-programing-languages","8":"category-python-development","9":"category-ibkr-quant-news","10":"category-quant-asia-pacific","11":"category-quant-development","12":"category-quant-regions","13":"tag-algo-trading","14":"tag-python","15":"tag-quant","16":"contributors-categories-quantinsti"},"pp_statuses_selecting_workflow":false,"pp_workflow_action":"current","pp_status_selection":"publish","acf":[],"yoast_head":"\nGini Index For Decision Trees – Part III | IBKR Quant<\/title>\n<meta name=\"description\" content=\"Gini Index for Decision Trees in Trading\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.interactivebrokers.com\/campus\/wp-json\/wp\/v2\/posts\/3555\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Gini Index For Decision Trees - 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