Trjtdtk

The variadic template constructor of my class cannot modify my class members, why is that so?

Keeping a retro style to sci-fi spaceships?

Finding the path in a graph from A to B then back to A with a minimum of shared edges

Difference between "generating set" and free product?

Hopping to infinity along a string of digits

Why did all the guest students take carriages to the Yule Ball?

Can withdrawing asylum be illegal?

What was the last x86 CPU that did not have the x87 floating-point unit built in?

First use of “packing” as in carrying a gun

Why can't wing-mounted spoilers be used to steepen approaches?

He got a vote 80% that of Emmanuel Macron’s

Arduino Pro Micro - switch off LEDs

Mortgage adviser recommends a longer term than necessary combined with overpayments

Does Parliament hold absolute power in the UK?

What are these Gizmos at Izaña Atmospheric Research Center in Spain?

Scientific Reports - Significant Figures

Can the DM override racial traits?

How to pronounce 1ターン?

Why is superheterodyning better than direct conversion?

How to delete random line from file using Unix command?

how can a perfect fourth interval be considered either consonant or dissonant?

Match Roman Numerals

Does the AirPods case need to be around while listening via an iOS Device?

What can I do if neighbor is blocking my solar panels intentionally?

Machine learning with sklearn vs. scipy stats

0

$begingroup$

I've created 50 random x and y points (with slope of y = 2x-1).

First, I used Linear Regression from sklearn to fit the model onto my dataset where I got a slope of 2.0066... and intercept of -0.535...

My Question: is fitting the model to our dataset considered training? For each given x value, since it has a y-value (supervised), does our machine go through each x,y match and create line of best fit based upon that? Thus, is our model trained?

Second, I used stats.linregress(x,y) from scipy to get slope and intercept (which seem really close if not the same to the slope and intercept I've got from using sklearn Linear Regression).

My Question: If both methods give the same result, why not just use scipy to get formula for the best fit line to make predictions? What is the benefit of using machine learning?

machine-learning python scikit-learn numpy scipy

share|improve this question

edited Apr 1 at 3:14

Ethan

697625

asked Mar 31 at 18:25

h_muskh_musk

1

$endgroup$

add a comment | 

0

$begingroup$

I've created 50 random x and y points (with slope of y = 2x-1).

First, I used Linear Regression from sklearn to fit the model onto my dataset where I got a slope of 2.0066... and intercept of -0.535...

My Question: is fitting the model to our dataset considered training? For each given x value, since it has a y-value (supervised), does our machine go through each x,y match and create line of best fit based upon that? Thus, is our model trained?

Second, I used stats.linregress(x,y) from scipy to get slope and intercept (which seem really close if not the same to the slope and intercept I've got from using sklearn Linear Regression).

My Question: If both methods give the same result, why not just use scipy to get formula for the best fit line to make predictions? What is the benefit of using machine learning?

machine-learning python scikit-learn numpy scipy

share|improve this question

edited Apr 1 at 3:14

Ethan

697625

asked Mar 31 at 18:25

h_muskh_musk

1

$endgroup$

add a comment | 

$begingroup$

I've created 50 random x and y points (with slope of y = 2x-1).

First, I used Linear Regression from sklearn to fit the model onto my dataset where I got a slope of 2.0066... and intercept of -0.535...

My Question: is fitting the model to our dataset considered training? For each given x value, since it has a y-value (supervised), does our machine go through each x,y match and create line of best fit based upon that? Thus, is our model trained?

Second, I used stats.linregress(x,y) from scipy to get slope and intercept (which seem really close if not the same to the slope and intercept I've got from using sklearn Linear Regression).

My Question: If both methods give the same result, why not just use scipy to get formula for the best fit line to make predictions? What is the benefit of using machine learning?

machine-learning python scikit-learn numpy scipy

share|improve this question

edited Apr 1 at 3:14

Ethan

697625

asked Mar 31 at 18:25

h_muskh_musk

1

$endgroup$

share|improve this question

edited Apr 1 at 3:14

Ethan

697625

asked Mar 31 at 18:25

h_muskh_musk

1

share|improve this question

edited Apr 1 at 3:14

Ethan

697625

asked Mar 31 at 18:25

h_muskh_musk

1

edited Apr 1 at 3:14

Ethan

697625

edited Apr 1 at 3:14

Ethan

697625

asked Mar 31 at 18:25

h_muskh_musk

1

asked Mar 31 at 18:25

h_muskh_musk

1

1 Answer
1

active

oldest

votes

1

$begingroup$

1. Yes fitting the data and finding the best fitting line is called training the model.


2. If you look at the source code of scikit-learn linear regression you can find the its using scipy linalg.lstsq module for finding the coefficients and intercept (most cases). See the source code for more details . Machine learning is fancy word for Application of mathematics (on data mostly) using computers (machines)

share|improve this answer

edited Mar 31 at 19:43

answered Mar 31 at 19:38

tamtam

1014

$endgroup$

add a comment | 

Your Answer

StackExchange.ready(function()
var channelOptions =
tags: "".split(" "),
id: "557"
;
initTagRenderer("".split(" "), "".split(" "), channelOptions);

StackExchange.using("externalEditor", function()
// Have to fire editor after snippets, if snippets enabled
if (StackExchange.settings.snippets.snippetsEnabled)
StackExchange.using("snippets", function()
createEditor();
);

else
createEditor();

);

function createEditor()
StackExchange.prepareEditor(
heartbeatType: 'answer',
autoActivateHeartbeat: false,
convertImagesToLinks: false,
noModals: true,
showLowRepImageUploadWarning: true,
reputationToPostImages: null,
bindNavPrevention: true,
postfix: "",
imageUploader:
brandingHtml: "Powered by u003ca class="icon-imgur-white" href="https://imgur.com/"u003eu003c/au003e",
contentPolicyHtml: "User contributions licensed under u003ca href="https://creativecommons.org/licenses/by-sa/3.0/"u003ecc by-sa 3.0 with attribution requiredu003c/au003e u003ca href="https://stackoverflow.com/legal/content-policy"u003e(content policy)u003c/au003e",
allowUrls: true
,
onDemand: true,
discardSelector: ".discard-answer"
,immediatelyShowMarkdownHelp:true
);



);

draft saved

draft discarded

Sign up or log in

StackExchange.ready(function ()
StackExchange.helpers.onClickDraftSave('#login-link');
);

Sign up using Google

Sign up using Facebook

Sign up using Email and Password

Post as a guest

Name

Email

Required, but never shown

StackExchange.ready(
function ()
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fdatascience.stackexchange.com%2fquestions%2f48308%2fmachine-learning-with-sklearn-vs-scipy-stats%23new-answer', 'question_page');

);

Post as a guest

Name

Email

Required, but never shown

1

$begingroup$

1. Yes fitting the data and finding the best fitting line is called training the model.


2. If you look at the source code of scikit-learn linear regression you can find the its using scipy linalg.lstsq module for finding the coefficients and intercept (most cases). See the source code for more details . Machine learning is fancy word for Application of mathematics (on data mostly) using computers (machines)

share|improve this answer

edited Mar 31 at 19:43

answered Mar 31 at 19:38

tamtam

1014

$endgroup$

add a comment | 

1

$begingroup$

1. Yes fitting the data and finding the best fitting line is called training the model.


2. If you look at the source code of scikit-learn linear regression you can find the its using scipy linalg.lstsq module for finding the coefficients and intercept (most cases). See the source code for more details . Machine learning is fancy word for Application of mathematics (on data mostly) using computers (machines)

share|improve this answer

edited Mar 31 at 19:43

answered Mar 31 at 19:38

tamtam

1014

$endgroup$

add a comment | 

$begingroup$

1. Yes fitting the data and finding the best fitting line is called training the model.


2. If you look at the source code of scikit-learn linear regression you can find the its using scipy linalg.lstsq module for finding the coefficients and intercept (most cases). See the source code for more details . Machine learning is fancy word for Application of mathematics (on data mostly) using computers (machines)

share|improve this answer

edited Mar 31 at 19:43

answered Mar 31 at 19:38

tamtam

1014

$endgroup$

share|improve this answer

edited Mar 31 at 19:43

answered Mar 31 at 19:38

tamtam

1014

answered Mar 31 at 19:38

tamtam

1014

answered Mar 31 at 19:38

tamtam

1014