Question-2 Python Machine LearningFor the California housing dataUse Regression Models to predict the house priceskNN RegressorDesicion Tree RegressorRandom Forest RegressorTry different hyperparameters to improve the scoresVisualize your resultsfrom sklearn.datasets import fetch_california_housingdataset_cal = fetch_california_housing() dataset_cal.keys() dict_keys(['data', 'target', 'frame', 'target_names', 'feature_names', 'DESCR'])

Oh no! Our experts couldn't answer your question.

Don't worry! We won't leave you hanging. Plus, we're giving you back one question for the inconvenience.

Submit your question and receive a step-by-step explanation from our experts in as fast as 30 minutes.

You have no more questions left.

Message from our expert:

Our experts are unable to provide you with a solution at this time. Try rewording your question, and make sure to submit one question at a time. We've credited a question to your account.

Your Question:

Question-2 Python Machine Learning

For the California housing data

Use Regression Models to predict the house prices
- kNN Regressor
- Desicion Tree Regressor
- Random Forest Regressor
Try different hyperparameters to improve the scores
Visualize your results

from sklearn.datasets import fetch_california_housing

dataset_cal = fetch_california_housing()

dataset_cal.keys()

dict_keys(['data', 'target', 'frame', 'target_names', 'feature_names', 'DESCR'])

Want to see the full answer?

Check out a sample Q&A here

See Solution

Knowledge Booster

Learn more about

Need a deep-dive on the concept behind this application? Look no further. Learn more about this topic, computer-science and related others by exploring similar questions and additional content below.

Similar questions