Attention follow the rule, you have to answer each question according to the instruction, before deliver. The document MUST BE RUN 100% ,and the stetment are well organized in each code (when you...

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Attention


follow the rule, you have to answer each question according to the instruction, before deliver.



The document MUST BE RUN 100% ,and the stetment are well organized in each code (when you coding each question ,you have to answer Why/Why-not.



The assignment is in the form of py and ipynb





  1. Both Gradient Boosting and Random Forests have a regression version of the algorithms as well. Try them out on the Boston ,diabetes, andcalifornia housingdatasets. How do they compare verse Ridge Regression? Do you think the feature importances make sense? Why/Why-not.

  2. On the regression datasets: Use RandomForest's feature importance scores to help you try and do some of your own feature engineering. This could involve making new features, or removing features. Once you've got your new features: compare Ridge Regression, Decision Trees, and Random Forest on the original features and your new ones. How does performance change for each algorithm? Why do you think your changes helped/hurt?

  3. (Optional) One of the most popular and well engineered Boosting algorithms is calledXGBoost. You can import it with the comandimport xgboost as xgb. XGBoost has better support for sparse datasets, and is often faster and more accurate than other Boosting implementations. Traing XGBoost on the same datasets you've used in this homework. How does it compare in terms of runtime and accuracy?


##############



  1. RandomForest, AdaBoost, and Gradient Boosting all provide methods for measuring feature importance. Use all three on the Forest Cover dataset. Do they agree about feature importances? Try to interperet the results, and tell me what you think.


Answered Same DayMar 14, 2021

Answer To: Attention follow the rule, you have to answer each question according to the instruction, before...

Ximi answered on Mar 14 2021
157 Votes
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.metrics import mean_squared_error"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Boston Dataset\n",
"1. GRADIENT BOOSTING\n",
"2. RANDOM FOREST REGRESSION \n",
"3. RIDGE REGRESSION\n",
"4. FEATURE IMPORTANCES"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"MSE for Gradient Boosting: 6.6579\n"
]
},
{
"data": {
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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"MSE for Random Forest: 10.2649\n",
"MSE for Ridge Regression: 16.3246\n"
]
}
],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
...
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