NeuroData Training Quiz 3
NeuroData Training Quiz
Test your knowledge on machine learning concepts with our engaging NeuroData Training Quiz. Designed for both beginners and aficionados, this quiz covers fundamental topics in data science and neural networks.
- 10 Multiple Choice Questions
- Assess your understanding of key machine learning principles
- Instant feedback on your performance
A value that defines the step taken at each iteration, before correction?
Gradient descent
learning rate
L2 regularization
L1 regularization
Adding a new feature to the model always results in equal or better performance on the training set?
True
False
Which of the statement about gradient descent is true?
We find local maxima in gradient descent
It is an optimization algorithm
Which of these metrics are used to evaluate classification algorithm?
AUC
Precision
F1 Score
Predicted vs True Chart
Which of these can be evaluated by a confusion matrix ?
F1 Score
Recall
Precision
All
Which of the following methods do we use to find the best fit line for data in Linear Regression?
Least Square Error
Maximum Likelihood
Logarithmic Loss
Both A and B
. What are the key challenges in successfully training machine learning model?
Features available in the data sets
Algorithms that are suitable for the task
Hyper parameters Tuning
Evaluation Metrics
_____ is a learning model that is used to identify a relationship between large amounts of information from a data set.
Classification
Multi class classification
Association
Unsupervised learning
The goal of clustering a set of data is to?
Choose the best data from the set
Divide them into groups of data that are near each other
Determine the nearest neighbors of each of the data
Predict the class of data
The following descriptions best describe what: 1. Value that has to be assigned manually. 2. The K value in K-nearest-neighbor is an example of this. 3. Value is set before the training.
Centroid
Model Parameter
Model hyper parameter
Significance
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