The p-value gives the probability of the null hypothesis is true. It gives us the statistical significance of our results. In other words, p-value determines the confidence of a model in a particular output.
In the context of machine learning interview questions, the p-value is often associated with statistical hypothesis testing rather than traditional machine learning algorithms. However, it’s important to recognize its relevance in certain situations.
The p-value is a measure used in statistical hypothesis testing to determine the evidence against a null hypothesis. In the context of machine learning, it might be employed when assessing the significance of a feature or the performance of a model.
Here’s a concise response:
“The p-value is a statistical measure used in hypothesis testing. In the context of machine learning, it can be useful in feature selection and model evaluation. For example, in feature selection, a low p-value for a feature suggests it is likely relevant to the target variable. In model evaluation, the p-value can be utilized to assess the significance of the observed performance metrics, helping to determine whether the model’s performance is statistically significant or occurred by chance.”
It’s worth noting that while the p-value has been widely used in statistics, there has been a growing awareness of its limitations and the importance of considering effect sizes and other measures. In some machine learning applications, alternative methods or a combination of techniques may be more appropriate for assessing significance and generalizing model performance.