This quiz is sponsored by DeepAlgorithms.in, a leading data science / machine learning training/consultancy provider (classroom coaching / online courses) based out of Hyderabad, India. Contact DeepAlgorithms to know details about their upcoming classroom/online training sessions. These questions can as well be used for checking/testing your for knowledge on data science for upcoming interviews. Following are some of the topics which are covered as part of this quiz:
- Classification
- Decision trees
- Ensemble model
- SVM
- KNN
In Decision Tree, by comparing the impurity across all possible splits in all possible Predictors, the next split is choosen. How we can measure the Impurity ?
Correct!
Wrong!
How we can avoid the overfitting in Decision Tree
Correct!
Wrong!
Predictive Errors are due to
Correct!
Wrong!
Random Forest Modeling (Ensemble Modeling) uses
Correct!
Wrong!
Gradient Boosting (Ensemble Modeling) uses
Correct!
Wrong!
Support Vector Machines are based on the concept of decision planes that define decision boundaries. Which technique used to identify the right hyperplances
Correct!
Wrong!
Which one is NOT used as a Kernel in Support Vector Machine Modeling
Correct!
Wrong!
To Identify the similar(neighbouring) records, K-Nearest Neighbous uses which distance metrics
Correct!
Wrong!
Machine Learning (Decision Trees, SVM) Quiz by DeepAlgorithms.in
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I have been recently working in the area of Data analytics including Data Science and Machine Learning / Deep Learning. I am also passionate about different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia, etc, and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data, etc. For latest updates and blogs, follow us on Twitter. I would love to connect with you on Linkedin.
Check out my latest book titled as First Principles Thinking: Building winning products using first principles thinking
Check out my latest book titled as First Principles Thinking: Building winning products using first principles thinking
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