How can you avoid overfitting in Machine Learning ?
By using a lot of data overfitting can be avoided, overfitting happens relatively as you have a small dataset, and you try to learn from it. But if you have a small database and you are forced to come with a model based on that. In such situation, you can use a technique known as cross validation. In this method the dataset splits into two section, testing and training datasets, the testing dataset will only test the model while, in training dataset, the datapoints will come up with the model. In this technique, a model is usually given a dataset of a known data on which training (training data set) is run and a dataset of unknown data against which the model is tested. The idea of cross validation is to define a dataset to “test” the model in the training phase.
Which class does not override the equals() and hashCode() methods, inheriting them directly from class Object?
java.lang.StringBuffer is the only class in the list that uses the default methods provided by class Object.
What is the difference between <div> and <frame>?
A <div> is a generic container element for grouping and styling, whereas a <frame> creates divisions within a web page and should be used within the <frameset> tag. The use of <frame> and <frameset> are no longer popular and are now being replaced with the more flexible <iframe>, which has become popular for embedding foreign elements (ie. Youtube videos) into a page.