ds 630 week1

pdf

School

Stevens Institute Of Technology *

*We aren’t endorsed by this school

Course

MIS637

Subject

Mechanical Engineering

Date

Feb 20, 2024

Type

pdf

Pages

2

Uploaded by MajorEnergyOryx33

Report
DS-630 WEEK-1 ASSIGNMENT (SPIRIT ID-1023554) Please complete the below problems. 1. Explain what Python environment you have set up on your system and if you have success on running through the command/code in Chapter 1 of the textbook. I have used this command to see my python version on my system python version It shows me Python 3.10.11 2. What are the two most common supervised tasks? The following are two of the most typical supervised learning tasks: Classification: Using the input data as a guide, forecast the individual class or category output. Examples include picture recognition, spam detection, and customer churn prediction. Regression is the process of using input data to predict a result in a continuous numerical form. Predicting stock prices, home values, or the length of a patient's stay are a few examples. 3. Can you name four common unsupervised tasks?
Four typical tasks for unsupervised learning are: Finding groupings or clusters within the collection of comparable data points, such in market segmentation, is known as clustering. Finding data that is noticeably different from expectations among the typical ones is known as anomaly detection. Learning association rules: Identifying connections between parameters in big datasets. Reducing the dimensionality of a large group of random variables by identifying a select few important ones. 4. If your model performs great on the training data but generalizes poorly to new instances, what is happening? Can you name three possible solutions? It is likely that a model has overfitting its initial data if it performs very well on the training set but poorly when applied to fresh cases. Three potential fixes consist of: Employ additional training data that is more illustrative of actual use scenarios. Try using regularization strategies that penalize the complexity of the model, such as L1/L2. Reduction of model complexity and capacity: simpler models in general Improve your generalization
Your preview ends here
Eager to read complete document? Join bartleby learn and gain access to the full version
  • Access to all documents
  • Unlimited textbook solutions
  • 24/7 expert homework help