How does the value of K affect the accuracy of the K nearest neighbors algorithm?
The K nearest neighbors (KNN) algorithm is a popular machine learning technique that is widely used for classification and regression tasks. It is a non-parametric method that makes predictions based on the similarity of the input data to its k nearest neighbors. The value of k, also known as the number of neighbors, plays a
- Published in Artificial Intelligence, EITC/AI/MLP Machine Learning with Python, Programming machine learning, Summary of K nearest neighbors algorithm, Examination review
How does underfitting differ from overfitting in terms of model performance?
Underfitting and overfitting are two common problems in machine learning models that can significantly impact their performance. In terms of model performance, underfitting occurs when a model is too simple to capture the underlying patterns in the data, resulting in poor predictive accuracy. On the other hand, overfitting happens when a model becomes too complex
- Published in Artificial Intelligence, EITC/AI/TFF TensorFlow Fundamentals, Overfitting and underfitting problems, Solving model’s overfitting and underfitting problems - part 2, Examination review
Explain the concept of underfitting and why it occurs in machine learning models.
Underfitting is a phenomenon that occurs in machine learning models when the model fails to capture the underlying patterns and relationships present in the data. It is characterized by high bias and low variance, resulting in a model that is too simple to accurately represent the complexity of the data. In this explanation, we will
- Published in Artificial Intelligence, EITC/AI/TFF TensorFlow Fundamentals, Overfitting and underfitting problems, Solving model’s overfitting and underfitting problems - part 1, Examination review