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+### Технологии: + +Библиотека Scikit-learn, библиотека numpy, библиотека matplotlib + +### Что делает лабораторная: + +В данной лабораторной работе мы сгенерировали данные, обучили и оценили три модели (линейную регрессию, полиномиальную регрессию и гребневую полиномиальную регрессию) на этих данных. Затем мы построили графики и вывели среднеквадратичную ошибку (MSE) для каждой модели. Графики позволяют нам визуализировать данные, их классификацию и границы решения моделей, а MSE показывает, насколько хорошо каждая модель соответствует данным. Чем меньше значение MSE, тем лучше модель соответствует данным. Меньшее значение MSE для полиномиальной регрессии указывает на то, что эта модель лучше соответствуют данным. + +### Как запустить: + +Лабораторная работа запускается в файле `shestakova_maria_lab_1.py` через Run: открывается окно и появляется вывод в консоли + +### Пример выходных значений: + +Консоль: + +![результат в консоли](MSE.png) + +Графики: + +![img2.png](linearRegGraphics.png) +![img3.png](polyRegGraphics.png) +![img4.png](ridgeRegGraphics.png) diff --git a/shestakova_maria_lab_1/linearRegGraphics.png b/shestakova_maria_lab_1/linearRegGraphics.png new file mode 100644 index 0000000000000000000000000000000000000000..d895d774cef8c9ef57abd4cf95c004e10cd7cdfb GIT binary patch literal 357130 zcmeFZbzGF)_BV_o(xE6QA*G-q3?&Uih=kJJN_VHkNQ-ng0|Fw_jnsfhNetcHT|><< z@ecQWpL6ct^Ly)$_xU{Uc|K=8pW(XLd+oLMwfEY4ueHAGns5~*SyCbzA{-nXQhB*o zYB)Hzf^l$gKM>r;mSFbdhjDO79Id6LRph0m8C6^yEv)U#ad70qlTz??)O)EuZYDo| z{J2KqR(|lptvI9n;2~xLx%+m(k7HF#-V!i-xT&_+Mm*JW(|k2Nem_=5=EW41WM^3f 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