FAQ on 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 … Read more

In machine learning, when a statistical model describes random error or noise instead of underlying relationship ‘overfitting’ occurs. When a model is excessively complex, overfitting is normally observed, because of having too many parameters with respect to the number of training data types. The model exhibits poor performance which has been overfit. In machine learning, … Read more

Machine learning relates with the study, design and development of the algorithms that give computers the capability to learn without being explicitly programmed. While, data mining can be defined as the process in which the unstructured data tries to extract knowledge or unknown interesting patterns. During this process machine, learning algorithms are used. Data mining … Read more

Machine learning is a branch of computer science which deals with system programming in order to automatically learn and improve with experience. For example: Robots are programed so that they can perform the task based on data they gather from sensors. It automatically learns programs from data. For a machine learning interview question asking “What … Read more

random forest uses bagging techniques whereas GBM uses boosting techniques. Random forests mainly try to reduce variance and GBM reduces both bias and variance of a model Random Forest and Gradient Boosting are both ensemble learning methods used in machine learning, but they differ in several key aspects: Algorithm Type: Random Forest is an ensemble … Read more