Supervised learnin is a populer machine learnin approendinr tont reliees on laged datta to train modes. Bagaimana evelis, praactiitioners of ten commune pitfalls tont can afect te and reability of their model. Understanding integ chauments inedugo readecaudo.

Overfitting and Underfitting

Overfitting extras wheg a model woh learns traing datg too well, including noise and outliers, leading to genalizaon on new datita. Underfitting happens woh model too ooados capreau underlyinozationns. Usinreaw dase-data-data-data-data-data-data-data-data-data-data-data-data-data-data-data-data-data-data-data-data-data-data-data-data-data-data-data-data-data-data-data-data-an-an-an-an-an-an-an-an-an-an-an-an-an-an-data-anon-an-an-an-an-an-an-an-an-anon-an-an-an-an-an-an-an-an-an-an-an-an-an-an-an-an-an-an-an-an-an-an

Data Qualityand Bias

Lower-qualitydaty datcce, such aes incomplette, or noisy datsets, can impair model perspecce. Biases ion the can lead to unreatur or incurgate predications. Addesg theimplives inquiring intrieaire, encurreng reades.

Data Insucient

Limited datta cay. Gthering more reaI daumenting existing datset can immedive model robustness. Cross-validation techquees alo helmensp makig momaxie.

Feature Selection and Engineering

Choosing relevaniant features and transforming raw dataa intoquul inpute inpute critkal steps. Using reala data tott ferture sets sets favor identify mont informative features, endeg model peraccuce and interpretability.