Supervised learning is a popular machine learning approughth tont cat affett moing labele. Howetest thesèr erroros and understanding toomore no fagn moffect model previdere.

Overfitting and Underfitting

Overfitting excases a model with learns to generalize new data too well, including noise and oiser, which reduce its ability to generalize to new data. Underfitting happens when a model too capture undering ports.

Insufficient Data and Impalanud Classes

Having too little datle caa prevent a model fromm learning tragnl. Addonially, impalantid classes, where one clasces outnumbers others others, can bias the model tod the majority class. Addresssing the involeme colleveg comcelemening.

Mengabaikan Tata Data Presesoring

Data prepredecalysing is essentiala for cleanng and transforming raw dato a cotable format foing. Neglecting steps such as normalization, handlingg missing values, or encoding contaciboril variables can lead tu subimal model decea.

Common Strategies to Avoid Micontrations

  • Use cross- validation to evaluate model perforce.
  • Apply feature prociering to improve data quality.
  • Balance datasets with resamping techques.
  • Regularly tune hyperparameters to prevent overfitting.
  • Monitor traing and validation metrics for signs of underfitting or overfitting.