{"id":9647,"date":"2023-05-04T20:50:14","date_gmt":"2023-05-04T20:50:14","guid":{"rendered":"https:\/\/labdeck.com\/?page_id=9647"},"modified":"2023-12-28T22:10:23","modified_gmt":"2023-12-28T22:10:23","slug":"regressions-in-python","status":"publish","type":"page","link":"https:\/\/labdeck.com\/ai-models\/regressions-in-python\/","title":{"rendered":"Regressions in Python"},"content":{"rendered":"\n

MD Python brings all of MatDeck\u2019s regression functions to Python, allowing users  to use highly optimized C++ functions in their Python code without the need of any complex syntax. With MD Python, Regressions can be done in one simple line, making specialized functions accessible to everyone.<\/p>\n\n\n

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We can compare MD Python to other third party Python libraries to see how much more accessible MD Python is. In the picture below we use MD Python to run a linear regression on our X and Y data.<\/p>\n\n\n\n

We only need 1<\/strong> line to run a linear regression and predict new values with the regression. We can use vectors as well to predict values for a large amount of new data.<\/p>\n\n\n\n

Compare that to the picture below, where we use SciKit Learn, the most popular library for regressions in Python.<\/p>\n\n\n

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As we can with SciKit Learn, we need 7 <\/strong>times more code to run the same regression and to predict a new value. Not only do you need 7<\/strong> times more code but you also have to reshape data and use confusing non-Pythonic functions to run a regression that takes 1<\/strong> line with MD Python. In the end, both regressions yield the same answer with MD Python offering a more precise answer.<\/p>\n\n\n\n

Regression Analysis with MD Python<\/h2>\n\n\n\n

Regression Analysis can be quickly and easily done in MatDeck with functions like regression<\/em><\/strong>, regressiontable<\/em><\/strong> and curveregtable. <\/em><\/strong>All MD Python Regression Analysis Function only require one line and allow you to access results immediately and draw conclusions with concise and accurate data. With our data, we can use a function like  regressiontable <\/em><\/strong>to find the perfect regression to run on our data without having to individually test and run several different regressions.  regressiontable offers all these answers in one line.<\/em><\/strong><\/p>\n\n\n

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As you can see we have regressiontable <\/em><\/strong>allows us to see which regression is the best fit and most accurate. With it, we can see that a Polynomial Regression to the 9th<\/sup> degree is a perfect fit for our data.<\/p>\n\n\n\n

Once again we can see how MD Python allows user to utilize complex mathematics with no experience. Regressions and Regression Analysis are now available, with simple syntax and fast functions, MD Python saves time and money.<\/p>\n\n\n\n

MD Python brings an extensive regressions library to Python, below you can see all Regression functions that can be used in Python:<\/p>\n\n\n\n