{"id":945,"date":"2024-07-14T14:17:10","date_gmt":"2024-07-14T11:17:10","guid":{"rendered":"https:\/\/acua.qcri.org\/blog\/?p=945"},"modified":"2024-07-15T11:35:11","modified_gmt":"2024-07-15T08:35:11","slug":"regression-vs-classification-model-selecting-the-ultimate-machine-learning-model","status":"publish","type":"post","link":"https:\/\/acua.qcri.org\/blog\/regression-vs-classification-model-selecting-the-ultimate-machine-learning-model\/","title":{"rendered":"Regression Vs Classification model: Selecting the ultimate machine learning model"},"content":{"rendered":"<figure id=\"attachment_946\" aria-describedby=\"caption-attachment-946\" style=\"width: 567px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-946 \" src=\"https:\/\/acua.qcri.org\/blog\/wp-content\/uploads\/2024\/07\/StockCake-Futuristic-Data-Interaction_1720445493-1024x574.jpg\" alt=\"\" width=\"567\" height=\"318\" srcset=\"https:\/\/acua.qcri.org\/blog\/wp-content\/uploads\/2024\/07\/StockCake-Futuristic-Data-Interaction_1720445493-1024x574.jpg 1024w, https:\/\/acua.qcri.org\/blog\/wp-content\/uploads\/2024\/07\/StockCake-Futuristic-Data-Interaction_1720445493-300x168.jpg 300w, https:\/\/acua.qcri.org\/blog\/wp-content\/uploads\/2024\/07\/StockCake-Futuristic-Data-Interaction_1720445493-768x430.jpg 768w, https:\/\/acua.qcri.org\/blog\/wp-content\/uploads\/2024\/07\/StockCake-Futuristic-Data-Interaction_1720445493.jpg 1456w\" sizes=\"(max-width: 567px) 100vw, 567px\" \/><figcaption id=\"caption-attachment-946\" class=\"wp-caption-text\"><em>Choose the best model to receive amazing results<\/em><\/figcaption><\/figure>\n<p><span style=\"font-weight: 400\">Machine learning mod<\/span><span style=\"background-color: var(--global--color-background);color: var(--global--color-primary);font-family: var(--global--font-secondary);font-size: var(--global--font-size-base)\">els have more to offer than what meets the eye. It is often known for uncovering hidden patterns and relationships between variables. The machine learning models provide us with the ability to predict future actions based on the current and past data available. If anything, machine learning is a door that is used to access the skill of making data-driven decision making. To clarify which model is suitable for your research, we should first differentiate the two. The regression model deals with continuous values like height, weight or temperature while the classification deals with distinct values like colours, shoe sizes or exam scores. Dividing the models based on their goals can help clarify what type of analysis they can be used for. For example, regression models aim to explain the y variable against the x variable so it is suitable for analysis that requires understanding the relationship between two variables. Ultimately, classification models aim to classify data into meaningful categories. This would be suitable for analysis that requires classifying products into different categories. Since both models are predictive, whichever one is chosen for the analysis will provide an additional benefit by predicting future actions.\u00a0<\/span><b style=\"color: var(--global--color-primary);font-family: var(--global--font-secondary);font-size: var(--global--font-size-base)\"><\/b><\/p>\n<p><span style=\"font-weight: 400\">The steps below are to ensure you select the right model:<\/span><\/p>\n<ol>\n<li><span style=\"font-weight: 400\"> Understand your data: by reading the data dictionary you can determine which type of data you are dealing with.\u00a0<\/span><\/li>\n<li><span style=\"font-weight: 400\"> Select attributes for analysis: narrow down your findings to help focus the analysis on the variables you want to utilize. This will help reduce the data and create more accurate models.<\/span><\/li>\n<li><span style=\"font-weight: 400\"> Choose a model to run: Depending on the attributes selected, you can choose to work with classification or regression. For example, if your attributes are mainly continuous it is recommended to use a regression model.\u00a0<\/span><\/li>\n<li><span style=\"font-weight: 400\"> Run a performance evaluation to ensure your model is accurate.<\/span><\/li>\n<\/ol>\n<p>Now go ahead and try the models on our exclusive system\u00a0<span style=\"font-weight: 400\">ACUA!! We offer a platform that helps beginners explore data analytics; try out their latest features now! Link: <\/span><a href=\"https:\/\/acua.qcri.org\/\"><span style=\"font-weight: 400\">https:\/\/acua.qcri.org\/<\/span><\/a><\/p>\n<p><span style=\"font-weight: 400\">To gain further knowledge on users, read some research below:<\/span><b>\u00a0<\/b><\/p>\n<p><span style=\"font-weight: 400\">Aldous, K. K., An, J., &amp; Jansen, B. J. (2019). Predicting Audience Engagement Across Social Media Platforms in the News Domain. <\/span><i><span style=\"font-weight: 400\">Lecture Notes in Computer Science<\/span><\/i><span style=\"font-weight: 400\">, 173\u2013187. <\/span><a href=\"https:\/\/doi.org\/10.1007\/978-3-030-34971-4_12\"><span style=\"font-weight: 400\">https:\/\/doi.org\/10.1007\/978-3-030-34971-4_12<\/span><\/a><\/p>\n<p><span style=\"font-weight: 400\">Aldous, K. K., An, J., &amp; Jansen, B. J. (2022). What really matters?: characterising and predicting user engagement of news postings using multiple platforms, sentiments and topics. <\/span><i><span style=\"font-weight: 400\">Behaviour &amp; Information Technology<\/span><\/i><span style=\"font-weight: 400\">, 1\u201324. <\/span><a href=\"https:\/\/doi.org\/10.1080\/0144929x.2022.2030798\"><span style=\"font-weight: 400\">https:\/\/doi.org\/10.1080\/0144929x.2022.2030798<\/span><\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Machine learning models have more to offer than what meets the eye. It is often known for uncovering hidden patterns and relationships between variables. The machine learning models provide us with the ability to predict future actions based on the current and past data available. If anything, machine learning is a door that is used&hellip; <a class=\"more-link\" href=\"https:\/\/acua.qcri.org\/blog\/regression-vs-classification-model-selecting-the-ultimate-machine-learning-model\/\">Continue reading <span class=\"screen-reader-text\">Regression Vs Classification model: Selecting the ultimate machine learning model<\/span><\/a><\/p>\n","protected":false},"author":14,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[57],"tags":[88,86,93,92,91,89,87,90,85],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v19.13 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Regression Vs Classification model: Selecting the ultimate machine learning model - Team Acua<\/title>\n<meta name=\"description\" content=\"What is the key difference between the regression model and the classsification model? How do we choose the best model for our analysis? 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