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Showing posts with label Data Science. Show all posts
Showing posts with label Data Science. Show all posts

Machine Learning MCQ's

1. If you have a basket of different fruit varieties with some prior information on size, color, shape of each and every fruit . Which learning methodology is best applicable?

   View Answer   

   super



2. If the outcome is binary(0/1), which model to be applied?

   View Answer   

   Logistic



3. For which one of these relationships could we use a regression analysis? Chose the correct one

   View Answer   

   HEIGHT



4. Which clustering technique requires prior knowledge of the number of clusters required?

   View Answer   

   k means



5. Which of the learning methodology applies conditional probability of all the variables with respective the dependent variable?

   View Answer   

   supervised



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