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Overfitting & Underfitting in Machine Learning
Overfitting in Machine Learning
Overfitting refers to a model that models the
training data too well.
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. The problem is that these concepts do not apply to new data and negatively impact the models ability to generalize.
Deep Learning basic MCQ's
1)What is the difference between the actual output and generated output known as?
Output Modulus
Accuracy
Cost
Output Difference
Answer:-Cost
(2)Recurrent Neural Networks are best suited for Text Processing.
True
False
Answer:-True
Machine Learning - Exploring the Model MCQ's
Machine Learning - Exploring the Model
Welcome to the course on Machine Learning - Exploring the Model
The objective of this course is to familiarize you with the steps involved in
fitting a machine learning model to a data set
You will learn all the concepts involved in building a Machine Learning Model,
from Hypothesis function that represents a model for a given data-set to evaluation of the hypothesis for a general case