The model you have trained has a low bias and high variance. How would you deal with it? Low bias occurs when the model is predicting values close to the actual value. It is mimicking the training dataset. The model has no generalization which means if the model is tested on unseen data...
40. Can you explain the bias-variance trade-off in machine learning? The bias-variance trade-off relates to the balance between underfitting (high bias) and overfitting (high variance) in machine learning models. Finding the optimal trade-off involves minimizing both bias and variance to achieve...
This also means that the data will have low variance and increased bias, adding to the dip in the accuracy of the model, alongside narrower confidence intervals. 6. What is an outlier? How can outliers be determined in a dataset? Outliers are data points that vary in a large way when ...
Gradient Boosting is a powerful ensemble technique known for its effectiveness in reducing bias and variance. It builds models sequentially, each new model correcting errors made by the previous ones. The result is a strong predictive performance that can outperform single models, especially on complex...
Variance:Error due to models that are too complex and fit the noise in the training data rather than the underlying pattern. High variance can lead to overfitting. The goal is to find a balance between bias and variance to minimize the total error and achieve good generalization to new data...
Laboratory method and its desirable specifications for imprecision (CVA), bias (B), and total error (TE) can be derived from biological variation data. b. Evaluating the clinical significance of changes in consecutive results from an individual: ...
Add more data to the training set and retrain the model using transfer learning to reduce the bias. Use a neural network model with more layers that are pretrained on ImageNet and apply transfer learning to increase the variance. Train a new model using the current neur...
1) What's the trade-off between bias and variance? [src] If our model is too simple and has very few parameters then it may have high bias and low variance. On the other hand if our model has large number of parameters then it’s going to have high variance and low bias. So we...
Which technique may help avoid bias and subjective decisions best? A. Bidder conference B. Weighting system C. Oral contract D. Letter of intent Correct Answer B. Weighting system ExplanationA weighting system can help avoid bias and subjective decisions by assigning specific weights or ...
I have two Gaussian curves, there are not samples, these are just probability distributions essentially. So I can do a Gaussian fit on them, or also a weighted average and weighted variance on the ... statistical-significance normal-distribution ...