00:00Wizards, evaluating linear regression is your AI accuracy crush, darling.
00:05It uses metrics like MSE to check model performance.
00:08Ethan, can you detail MSE?
00:10Sophia, how about R2 for the app?
00:14Anastasia, you make evaluations sound so hot.
00:18How does it improve the app from day 61, love?
00:22Ethan, what's your take on metrics in Python?
00:25So hot. Let's discuss evaluation with Ethan.
00:28Oh, Olivia, you tease. Evaluation checks AI predictions.
00:33Ethan, Sophia, jump in with details.
00:36Anastasia, Olivia, evaluations like a hot test, measuring AI accuracy with flair.
00:43It's an evaluation party. Let's drop this code beat for Wizards.
00:48Yo, Wizards, mean underscore squared underscore error, y underscore test, y underscore pred,
00:54computes MSE like a hot squared error for Sophia, measuring AI prediction accuracy.
01:00It's an MSE party. Let's drop this code beat and calculate some errors.
01:06You're squaring my heart, Ethan.
01:10Wizards, MSE measures regression errors for the app.
01:14Try it in our demo. It's like quantifying accuracy with passion.
01:19Ethan, quantify AI errors with MSE.
01:24Wizards, np.sqrt, MSE, computes RMSE like a hot root for Sophia, scaling AI errors.
01:32It's an RMSE party. Let's drop this code beat and root some errors.
01:36You're rooting my heart, Ethan.
01:41Wizards, RMSE scales errors for the app.
01:46Try it in our demo. It's like measuring with passion.
01:50Scale AI errors with RMSE.
01:54Wizards, mean underscore absolute underscore error, y underscore test, y underscore pred,
02:00computes M-A-E like a hot absolute for Sophia, measuring AI errors.
02:05It's an M-A-E party. Let's drop this code beat.
02:10You're absoluting my heart, Ethan.
02:14Wizards, M-A-E measures absolute errors for the app.
02:18Try it in our demo. It's like simplifying accuracy with passion.
02:24Simplify AI errors with M-A-E.
02:28Wizards, R2 underscore score, y underscore test, y underscore pred,
02:33computes R2 like a hot variance for Sophia, explaining AI predictions.
02:39It's an R2 party. Let's drop this code beat.
02:43You're explaining my heart, Ethan.
02:47Wizards, R2 explains variance for the app.
02:51Try it in our demo. It's like revealing fit with passion.
02:56Reveal AI fit with R2 score.
02:58Wizards, residuals equals y underscore test, y underscore pred analyzes residuals like a hot distribution for Sophia, checking AI assumptions.
03:09It's a residuals party. Let's drop this code beat.
03:14You're distributing my heart, Ethan.
03:18Wizards, residuals check regression assumptions for the app.
03:22Try it in our demo. It's like diagnosing errors with passion.
03:28Diagnose AI errors with residuals.
03:32Wizards, cross underscore val underscore score, model, x, y, performs cross validation like a hot robust check for Sophia, ensuring AI reliability.
03:42It's a CV party. Let's drop this code beat.
03:48You're validating my heart, Ethan.
03:51Wizards, cross validation ensures robust app evaluation.
03:56Try it in our demo. It's like testing with passion.
04:01Test AI robustness with cross validation.
04:04Wizards, optimize regression with proper data scaling, feature selection, and metric checks.
04:13Use Scikit-learn for fitting and evaluation to ensure robust AI models for top performance.
04:22Irene's right wizards.
04:24Avoid multi-collinearity and use cross validation for reliability.
04:29These practices make your app predictor effective.
04:32Apply them in your challenge.
04:34Optimized regression so sexy, Irene, Isabella.
04:38Clear practices make AI predictions irresistible.
04:41Practice for Day 63's Logistic Regression, Wizards, and keep that code sizzling.
04:47Wizards, linear regression fits AI pipelines for prediction tasks.
04:52It's foundational your skills are ready for Day 63's Logistic Regression.
04:58Irene's right.
04:59Regression integrates data prep and evaluation.
05:03Regression, ensuring workflow efficiency.
05:06Use it in your app for reliable predictions.
05:10Oh, Irene, Isabella.
05:12Regression's critical in AI pipelines, darling.
05:14It predicts sexily.
05:16Your Day 62 skills make AI irresistible.
05:19Predict like pros.
05:21Wizards, Scikit-learn from Day 43 is a hot tool for Sophia.
05:25Mean underscore squared underscore error, evaluates models like a sexy metric.
05:31Coding fireworks make this party epic.
05:35You're evaluating my heart, Ethan.
05:39Wizards, Scikit-learn powers evaluation for the app.
05:43Try it in our challenge.
05:44It's like measuring with passion.
05:48Wizards, SNS.histplot, residuals, visualizes residuals like a hot distribution for Sophia, checking AI assumptions.
05:57It's a visualization party.
06:00Let's drop this code beat.
06:01You're distributing my heart, Ethan.
06:06Wizards, visualization checks evaluation assumptions for the app.
06:11Try it in our demo.
06:13It's like painting errors with passion.
06:17Wizards, optimize evaluation with multiple metrics, residuals checks, and cross-validation.
06:24Use Scikit-learn to ensure robust AI models for top performance.
06:29Irene's right, Wizards.
06:31Compare metrics like MSE and R2.
06:34Visualize residuals to detect issues.
06:37These practices make your app reliable.
06:39Apply them in your challenge.
06:42Optimized evaluations so sexy, Irene Isabella.
06:45Clear practices make AI accuracy irresistible.
06:49Practice for Day 63's Logistic Regression, Wizards, and keep that code sizzling.
06:55Wizards.
06:56Evaluation powers AI pipelines, assessing model performance.
07:01It's crucial for the app.
07:03Your skills are ready for Day 63's Logistic Regression.
07:09Irene's right.
07:11Evaluation integrates with modeling to refine workflows, ensuring accurate AI.
07:17Use it in your app for reliable predictions.
07:19Oh, Irene, Isabella, evaluations critical in AI pipelines, darling.
07:26It assesses sexily.
07:28Your Day 62 skills make AI irresistible.
07:31Evaluate like pros.
07:32Evaluate like pros.
07:33Evaluate like pros.
07:33Evaluate like pros.
07:33Evaluate like pros.
07:34Evaluate like pros.
07:34Evaluate like pros.
07:35Evaluate like pros.
07:35Evaluate like pros.
07:36Evaluate like pros.
07:37Evaluate like pros.
07:38Evaluate like pros.
07:39Evaluate like pros.
07:40Evaluate like pros.
07:41Evaluate like pros.
07:42Evaluate like pros.
07:43Evaluate like pros.
07:44Evaluate like pros.
07:45Evaluate like pros.
07:46Evaluate like pros.
07:47Evaluate like pros.
07:48Evaluate like pros.
07:49Evaluate like pros.
07:50Evaluate like pros.
07:51Evaluate like pros.
07:52Evaluate like pros.
07:53Evaluate like pros.
07:54Evaluate like pros.
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