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View Code? Open in Web Editor NEWMachine Learning Foundations: Linear Algebra, Calculus, Statistics & Computer Science
License: MIT License
Machine Learning Foundations: Linear Algebra, Calculus, Statistics & Computer Science
License: MIT License
In 6-statistics.ipynb
the result of Dream Island Adélie question isn't correct. At In[133] f and m contains all the Adélie penguins, not just the once on Dream Island. The correct answer is t=-2.43, p=0.0179.
(In[137]: tp.pvalue is nicer than tp[1] )
Hello,
Something went wrong while trying to access
ML-foundations/notebooks/2-linear-algebra-ii.ipynb
line 5709 of 2-linear-algebra-ii.ipynb
"* Non-zero singular values of eigenvectors eigenvalues of eigenvectors eigenvalues of
Code "from sklearn.datasets import load_boston" in 6-statistics.ipynb in Ordinary Least Squares Exercises Section is throwing Error in CoLab because load_boston
has been removed from scikit-learn since version 1.2
File : https://github.com/jonkrohn/ML-foundations/blob/master/notebooks/6-statistics.ipynb
Section : Ordinary Least Squares Exercises
Code :
from sklearn.datasets import load_boston
Error :
ImportError Traceback (most recent call last)
in <cell line: 1>()
----> 1 from sklearn.datasets import load_boston
/usr/local/lib/python3.10/dist-packages/sklearn/datasets/init.py in getattr(name)
154 """
155 )
--> 156 raise ImportError(msg)
157 try:
158 return globals()[name]
ImportError:
load_boston
has been removed from scikit-learn since version 1.2.
The Boston housing prices dataset has an ethical problem: as
investigated in [1], the authors of this dataset engineered a
non-invertible variable "B" assuming that racial self-segregation had a
positive impact on house prices [2]. Furthermore the goal of the
research that led to the creation of this dataset was to study the
impact of air quality but it did not give adequate demonstration of the
validity of this assumption.
The scikit-learn maintainers therefore strongly discourage the use of
this dataset unless the purpose of the code is to study and educate
about ethical issues in data science and machine learning.
In this special case, you can fetch the dataset from the original
source::
import pandas as pd
import numpy as np
data_url = "http://lib.stat.cmu.edu/datasets/boston"
raw_df = pd.read_csv(data_url, sep="\s+", skiprows=22, header=None)
data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])
target = raw_df.values[1::2, 2]
Alternative datasets include the California housing dataset and the
Ames housing dataset. You can load the datasets as follows::
from sklearn.datasets import fetch_california_housing
housing = fetch_california_housing()
for the California housing dataset and::
from sklearn.datasets import fetch_openml
housing = fetch_openml(name="house_prices", as_frame=True)
for the Ames housing dataset.
[1] M Carlisle.
"Racist data destruction?"
https://medium.com/@docintangible/racist-data-destruction-113e3eff54a8
[2] Harrison Jr, David, and Daniel L. Rubinfeld.
"Hedonic housing prices and the demand for clean air."
Journal of environmental economics and management 5.1 (1978): 81-102.
https://www.researchgate.net/publication/4974606_Hedonic_housing_prices_and_the_demand_for_clean_air
NOTE: If your import is failing due to a missing package, you can
manually install dependencies using either !pip or !apt.
To view examples of installing some common dependencies, click the
"Open Examples" button below.
Originally posted by ScoobyQ May 28, 2023
Hi,
Does anyone have the links, please?
I can see the notebooks on GitHub:
The YT videos seem to end during section 6 with:
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TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
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JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.