Contents
What can cause overfitting?
Overfitting happens when a model learns the detail and noise in the training data to the extent that it negatively impacts the performance of the model on new data. This means that the noise or random fluctuations in the training data is picked up and learned as concepts by the model.
How do you normalize text?
Text normalization is the process of transforming a text into a canonical (standard) form. For example, the word “gooood” and “gud” can be transformed to “good”, its canonical form. Another example is mapping of near identical words such as “stopwords”, “stop-words” and “stop words” to just “stopwords”.
Why is my ML model is not working?
Hint: Problem Complexity, Model Complexity, and Sample Size should be in line. When studying a machine learning course, you easily deploy and test machine learning models in the course projects. Everything works well. However, in real-life scenarios, your model just creates meaningless results and you may be unsure what the issue is.
What makes a ML model a good model?
Most of the ML models return a good result. f) Model complexity= High, Problem complexity = Low, Sample size= Large: A complex model may return a slightly better result compared to a typical model. Considering explainability and maintenance of the model, you may choose to use the simpler model.
What does AutoML stand for in machine learning?
Automated machine learning, also referred to as automated ML or AutoML, is the process of automating the time-consuming, iterative tasks of machine learning model development. It allows data scientists, analysts, and developers to build ML models with high scale, efficiency, and productivity all while sustaining model quality.
How is machine learning used for text summarization?
In that framework, a generative model takes the original text as input and generates the summary using reinforcement learning to optimize the generator for a highly rewarded summary. Further, a discriminator model tries to distinguish the ground truth summaries from the generated summaries by the generator.