Data Science interview questions covering Machine Learning , Deep Learning, Natural Language Processing and more.
There are three parts to this answer. What is overfitting and underfitting Why do they occur How can you overcome both of them. Overfitting is the result of over training the model while underfitting is the result of keeping the model too simple, both leading to high generalization error. Overtraining leads to a more complex…
We use test set such that we build a model that generalizes well on unseen dataset. If we use test set in tuning for hyper-parameters to select the model, we’re indirectly using test set in training or rather, our model has seen the test set. Hence, it is no longer an unseen dataset but already…
Before any model is built for the problem in hand, the entire dataset exists as a single entity. One can start learning from this dataset and use the built models to make predictions on unseen data. The later part is called generalisation in Machine Learning terminology. Training on entire dataset leads to an overfitted model…
Less amount of data Simple Model like a linear classifier Complex Model like a classifier of high degree polynomial All of the above Answer – (1), (3) Overfitting generally happens if the model tries to fit everything because it is too complex or there is too less amount of data. When your model performs well on…