Recent searches

No recent searches

Search options

Only available when logged in.
toad.social is one of the many independent Mastodon servers you can use to participate in the fediverse.
Mastodon server operated by David Troy, a tech pioneer and investigative journalist addressing threats to democracy. Thoughtful participation and discussion welcome.

Administered by:

Server stats:

218
active users

toad.social: AboutProfiles directoryPrivacy policy

Mastodon: AboutGet the appKeyboard shortcutsView source codev4.4.1

#linearregression

0 posts0 participants0 posts today
IT News<p>Understanding Linear Regression - Although [Vitor Fróis] is explaining linear regression because it relates to machi... - <a href="https://hackaday.com/2025/05/08/understanding-linear-regression/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">hackaday.com/2025/05/08/unders</span><span class="invisible">tanding-linear-regression/</span></a> <a href="https://schleuss.online/tags/linearregression" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>linearregression</span></a> <a href="https://schleuss.online/tags/machinelearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>machinelearning</span></a> <a href="https://schleuss.online/tags/math" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>math</span></a></p>
cathill<p>Is machine learning merely a form of curve-fitting?<br><a href="https://mastodon.social/tags/machinelearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>machinelearning</span></a> <a href="https://mastodon.social/tags/ai" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ai</span></a> <a href="https://mastodon.social/tags/curvefitting" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>curvefitting</span></a> <a href="https://mastodon.social/tags/linearregression" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>linearregression</span></a> <a href="https://mastodon.social/tags/buzzwords" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>buzzwords</span></a></p>
Eric Maugendre<p><span class="h-card" translate="no"><a href="https://a.gup.pe/u/data" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>data</span></a></span> <span class="h-card" translate="no"><a href="https://a.gup.pe/u/datadon" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>datadon</span></a></span> 🧵</p><p>Accuracy! To counter regression dilution, a method is to add a constraint on the statistical modeling.<br>Regression Redress restrains bias by segregating the residual values.<br>My article: <a href="http://data.yt/kit/regression-redress.html" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">http://</span><span class="ellipsis">data.yt/kit/regression-redress</span><span class="invisible">.html</span></a></p><p><a href="https://hachyderm.io/tags/bias" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>bias</span></a> <a href="https://hachyderm.io/tags/modeling" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>modeling</span></a> <a href="https://hachyderm.io/tags/dataDev" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>dataDev</span></a> <a href="https://hachyderm.io/tags/AIDev" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AIDev</span></a> <a href="https://hachyderm.io/tags/modelEvaluation" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>modelEvaluation</span></a> <a href="https://hachyderm.io/tags/regression" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>regression</span></a> <a href="https://hachyderm.io/tags/modelling" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>modelling</span></a> <a href="https://hachyderm.io/tags/dataLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>dataLearning</span></a> <a href="https://hachyderm.io/tags/linearRegression" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>linearRegression</span></a> <a href="https://hachyderm.io/tags/probability" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>probability</span></a> <a href="https://hachyderm.io/tags/probabilities" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>probabilities</span></a> <a href="https://hachyderm.io/tags/statistics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>statistics</span></a> <a href="https://hachyderm.io/tags/stats" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>stats</span></a> <a href="https://hachyderm.io/tags/correctionRatio" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>correctionRatio</span></a> <a href="https://hachyderm.io/tags/ML" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ML</span></a> <a href="https://hachyderm.io/tags/distributions" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>distributions</span></a> <a href="https://hachyderm.io/tags/accuracy" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>accuracy</span></a> <a href="https://hachyderm.io/tags/RegressionRedress" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>RegressionRedress</span></a> <a href="https://hachyderm.io/tags/Python" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Python</span></a> <a href="https://hachyderm.io/tags/RStats" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>RStats</span></a></p>
Eric Maugendre<p><span class="h-card" translate="no"><a href="https://a.gup.pe/u/data" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>data</span></a></span> <span class="h-card" translate="no"><a href="https://a.gup.pe/u/datadon" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>datadon</span></a></span> 🧵</p><p>How to assess a statistical model?<br>How to choose between variables?</p><p>Pearson's <a href="https://hachyderm.io/tags/correlation" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>correlation</span></a> is irrelevant if you suspect that the relationship is not a straight line.</p><p>If monotonic relationship:<br>"<a href="https://hachyderm.io/tags/Spearman" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Spearman</span></a>’s rho is particularly useful for small samples where weak correlations are expected, as it can detect subtle monotonic trends." It is "widespread across disciplines where the measurement precision is not guaranteed".<br>"<a href="https://hachyderm.io/tags/Kendall" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Kendall</span></a>’s Tau-b is less affected [than Spearman’s rho] by outliers in the data, making it a robust option for datasets with extreme values."<br>Ref: <a href="https://statisticseasily.com/kendall-tau-b-vs-spearman/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">statisticseasily.com/kendall-t</span><span class="invisible">au-b-vs-spearman/</span></a></p><p><a href="https://hachyderm.io/tags/normality" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>normality</span></a> <a href="https://hachyderm.io/tags/normalDistribution" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>normalDistribution</span></a> <a href="https://hachyderm.io/tags/modeling" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>modeling</span></a> <a href="https://hachyderm.io/tags/dataDev" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>dataDev</span></a> <a href="https://hachyderm.io/tags/AIDev" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AIDev</span></a> <a href="https://hachyderm.io/tags/ML" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ML</span></a> <a href="https://hachyderm.io/tags/modelEvaluation" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>modelEvaluation</span></a> <a href="https://hachyderm.io/tags/regression" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>regression</span></a> <a href="https://hachyderm.io/tags/modelling" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>modelling</span></a> <a href="https://hachyderm.io/tags/dataLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>dataLearning</span></a> <a href="https://hachyderm.io/tags/featureEngineering" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>featureEngineering</span></a> <a href="https://hachyderm.io/tags/linearRegression" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>linearRegression</span></a> <a href="https://hachyderm.io/tags/modeling" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>modeling</span></a> <a href="https://hachyderm.io/tags/probability" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>probability</span></a> <a href="https://hachyderm.io/tags/probabilities" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>probabilities</span></a> <a href="https://hachyderm.io/tags/statistics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>statistics</span></a> <a href="https://hachyderm.io/tags/stats" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>stats</span></a> <a href="https://hachyderm.io/tags/correctionRatio" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>correctionRatio</span></a> <a href="https://hachyderm.io/tags/ML" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ML</span></a> <a href="https://hachyderm.io/tags/Pearson" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Pearson</span></a> <a href="https://hachyderm.io/tags/bias" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>bias</span></a> <a href="https://hachyderm.io/tags/regressionRedress" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>regressionRedress</span></a> <a href="https://hachyderm.io/tags/distributions" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>distributions</span></a></p>
Eric Maugendre<p><span class="h-card" translate="no"><a href="https://a.gup.pe/u/data" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>data</span></a></span> <span class="h-card" translate="no"><a href="https://a.gup.pe/u/datadon" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>datadon</span></a></span> 🧵</p><p>Redressing <a href="https://hachyderm.io/tags/Bias" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Bias</span></a>: "Correlation Constraints for Regression Models":<br>Treder et al (2021) <a href="https://doi.org/10.3389/fpsyt.2021.615754" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">doi.org/10.3389/fpsyt.2021.615</span><span class="invisible">754</span></a></p><p><a href="https://hachyderm.io/tags/dataDev" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>dataDev</span></a> <a href="https://hachyderm.io/tags/linearRegression" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>linearRegression</span></a> <a href="https://hachyderm.io/tags/modeling" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>modeling</span></a> <a href="https://hachyderm.io/tags/probability" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>probability</span></a> <a href="https://hachyderm.io/tags/probabilities" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>probabilities</span></a> <a href="https://hachyderm.io/tags/statistics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>statistics</span></a> <a href="https://hachyderm.io/tags/stats" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>stats</span></a> <a href="https://hachyderm.io/tags/modelling" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>modelling</span></a> <a href="https://hachyderm.io/tags/regression" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>regression</span></a> <a href="https://hachyderm.io/tags/correctionRatio" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>correctionRatio</span></a> <a href="https://hachyderm.io/tags/skLearn" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>skLearn</span></a> <a href="https://hachyderm.io/tags/scikitLearn" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>scikitLearn</span></a> <a href="https://hachyderm.io/tags/python" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>python</span></a> <a href="https://hachyderm.io/tags/AIDev" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AIDev</span></a></p>
Eric Maugendre<p>"In real life, we weigh the anticipated consequences of the decisions that we are about to make. That approach is much more rational than limiting the percentage of making the error of one kind in an artificial (null hypothesis) setting or using a measure of evidence for each model as the weight."<br>Longford (2005) <a href="http://www.stat.columbia.edu/~gelman/stuff_for_blog/longford.pdf" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">http://www.</span><span class="ellipsis">stat.columbia.edu/~gelman/stuf</span><span class="invisible">f_for_blog/longford.pdf</span></a></p><p><a href="https://hachyderm.io/tags/modeling" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>modeling</span></a> <a href="https://hachyderm.io/tags/nullHypothesis" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>nullHypothesis</span></a> <a href="https://hachyderm.io/tags/probability" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>probability</span></a> <a href="https://hachyderm.io/tags/probabilities" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>probabilities</span></a> <a href="https://hachyderm.io/tags/pValues" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>pValues</span></a> <a href="https://hachyderm.io/tags/statistics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>statistics</span></a> <a href="https://hachyderm.io/tags/stats" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>stats</span></a> <a href="https://hachyderm.io/tags/statisticalLiteracy" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>statisticalLiteracy</span></a> <a href="https://hachyderm.io/tags/bias" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>bias</span></a> <a href="https://hachyderm.io/tags/inference" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>inference</span></a> <a href="https://hachyderm.io/tags/modelling" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>modelling</span></a> <a href="https://hachyderm.io/tags/regression" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>regression</span></a> <a href="https://hachyderm.io/tags/linearRegression" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>linearRegression</span></a></p>
Eric Maugendre<p>Feature Selection in Python; a script ready to use: <a href="https://johfischer.com/2021/08/06/correlation-based-feature-selection-in-python-from-scratch/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">johfischer.com/2021/08/06/corr</span><span class="invisible">elation-based-feature-selection-in-python-from-scratch/</span></a></p><p><a href="https://hachyderm.io/tags/interpretability" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>interpretability</span></a> <a href="https://hachyderm.io/tags/featureSelection" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>featureSelection</span></a> <a href="https://hachyderm.io/tags/python" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>python</span></a> <a href="https://hachyderm.io/tags/probability" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>probability</span></a> <a href="https://hachyderm.io/tags/probabilities" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>probabilities</span></a> <a href="https://hachyderm.io/tags/bigData" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>bigData</span></a> <a href="https://hachyderm.io/tags/classification" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>classification</span></a> <a href="https://hachyderm.io/tags/linearRegression" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>linearRegression</span></a> <a href="https://hachyderm.io/tags/regression" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>regression</span></a> <a href="https://hachyderm.io/tags/Schusterbauer" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Schusterbauer</span></a> <a href="https://hachyderm.io/tags/inference" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>inference</span></a> <a href="https://hachyderm.io/tags/AIDev" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AIDev</span></a></p>
Towards Data Science<p>In Elisa Yao's newest article, she breaks down the process of implementing Linear Regression in Python using a simple dataset known as “Boston Housing”, step by step. </p><p><a href="https://me.dm/tags/LinearRegression" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>LinearRegression</span></a> <a href="https://me.dm/tags/Python" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Python</span></a></p><p><a href="https://towardsdatascience.com/predict-housing-price-using-linear-regression-in-python-bfc0fcfff640" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">towardsdatascience.com/predict</span><span class="invisible">-housing-price-using-linear-regression-in-python-bfc0fcfff640</span></a></p>
Eric Maugendre<p>"Feature importance helps in understanding which features contribute most to the prediction"</p><p>A few lines with <a href="https://hachyderm.io/tags/sklearn" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>sklearn</span></a>: <a href="https://mljourney.com/sklearn-linear-regression-feature-importance/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">mljourney.com/sklearn-linear-r</span><span class="invisible">egression-feature-importance/</span></a> </p><p><a href="https://hachyderm.io/tags/interpretability" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>interpretability</span></a> <a href="https://hachyderm.io/tags/explainability" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>explainability</span></a> <a href="https://hachyderm.io/tags/AIethics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AIethics</span></a> <a href="https://hachyderm.io/tags/compliance" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>compliance</span></a> <a href="https://hachyderm.io/tags/taxonomy" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>taxonomy</span></a> <a href="https://hachyderm.io/tags/ethicalAI" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ethicalAI</span></a> <a href="https://hachyderm.io/tags/AIevaluation" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AIevaluation</span></a> <a href="https://hachyderm.io/tags/linearRegression" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>linearRegression</span></a> <a href="https://hachyderm.io/tags/featureEngineering" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>featureEngineering</span></a></p>
Eric Maugendre<p><span class="h-card" translate="no"><a href="https://a.gup.pe/u/datadon" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>datadon</span></a></span></p><p><a href="https://hachyderm.io/tags/Lasso" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Lasso</span></a> <a href="https://hachyderm.io/tags/LinearRegression" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>LinearRegression</span></a> "is useful in some contexts due to its tendency to prefer solutions with fewer non-zero coefficients, effectively reducing the number of features upon which the given solution is dependent"</p><p><a href="https://scikit-learn.org/stable/modules/linear_model.html#lasso" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">scikit-learn.org/stable/module</span><span class="invisible">s/linear_model.html#lasso</span></a> 🧵</p><p><a href="https://hachyderm.io/tags/dataDev" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>dataDev</span></a> <a href="https://hachyderm.io/tags/AIDev" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AIDev</span></a> <a href="https://hachyderm.io/tags/ML" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ML</span></a> <a href="https://hachyderm.io/tags/sklearn" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>sklearn</span></a> <a href="https://hachyderm.io/tags/python" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>python</span></a> <a href="https://hachyderm.io/tags/interpretability" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>interpretability</span></a></p>
Eric Maugendre<p><span class="h-card" translate="no"><a href="https://a.gup.pe/u/datadon" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>datadon</span></a></span></p><p>"The following sections discuss several state-of-the-art interpretable and explainable <a href="https://hachyderm.io/tags/ML" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ML</span></a> methods. The selection of works does not comprise an exhaustive survey of the literature. Instead, it is meant to illustrate the commonest properties and inductive biases behind interpretable models and [black-box] explanation methods using concrete instances."<br><a href="https://wires.onlinelibrary.wiley.com/doi/full/10.1002/widm.1493#widm1493-sec-0010-title" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">wires.onlinelibrary.wiley.com/</span><span class="invisible">doi/full/10.1002/widm.1493#widm1493-sec-0010-title</span></a> 🧵</p><p><a href="https://hachyderm.io/tags/interpretability" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>interpretability</span></a> <a href="https://hachyderm.io/tags/explainability" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>explainability</span></a> <a href="https://hachyderm.io/tags/aiethics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>aiethics</span></a> <a href="https://hachyderm.io/tags/compliance" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>compliance</span></a> <a href="https://hachyderm.io/tags/taxonomy" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>taxonomy</span></a> <a href="https://hachyderm.io/tags/ethicalai" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ethicalai</span></a> <a href="https://hachyderm.io/tags/aievaluation" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>aievaluation</span></a> <a href="https://hachyderm.io/tags/linearRegression" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>linearRegression</span></a></p>
IB Teguh TM<p><a href="https://mastodon.social/tags/LinearRegression" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>LinearRegression</span></a> <a href="https://mastodon.social/tags/Python" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Python</span></a> <a href="https://mastodon.social/tags/Sklearn" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Sklearn</span></a><br>Dive into predictive modeling with our comprehensive guide on linear regression using Python and sklearn. Learn step-by-step implementation, result interpretation, and data visualization techniques. Perfect for beginners</p><p><a href="https://teguhteja.id/mastering-linear-regression-with-python-and-sklearn-a-step-by-step-guide/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">teguhteja.id/mastering-linear-</span><span class="invisible">regression-with-python-and-sklearn-a-step-by-step-guide/</span></a></p>
Eric Maugendre<p>An easy guide to predict possible future quantities, by Mercy Kibet: <a href="https://www.influxdata.com/blog/guide-regression-analysis-time-series-data/#heading0" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">influxdata.com/blog/guide-regr</span><span class="invisible">ession-analysis-time-series-data/#heading0</span></a></p><p><a href="https://hachyderm.io/tags/timeSeries" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>timeSeries</span></a> <a href="https://hachyderm.io/tags/data" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>data</span></a> <a href="https://hachyderm.io/tags/inference" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>inference</span></a> <a href="https://hachyderm.io/tags/linearRegression" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>linearRegression</span></a> <a href="https://hachyderm.io/tags/dataScience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>dataScience</span></a> <a href="https://hachyderm.io/tags/futures" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>futures</span></a> <a href="https://hachyderm.io/tags/money" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>money</span></a> <a href="https://hachyderm.io/tags/trends" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>trends</span></a> <a href="https://hachyderm.io/tags/Python" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Python</span></a></p>
Rohit Farmer, Ph.D.<p>A <a href="https://fosstodon.org/tags/tutorial" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>tutorial</span></a> on building and <a href="https://fosstodon.org/tags/interpreting" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>interpreting</span></a> <a href="https://fosstodon.org/tags/linearRegression" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>linearRegression</span></a> models for <a href="https://fosstodon.org/tags/inferential" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>inferential</span></a> and <a href="https://fosstodon.org/tags/predictive" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>predictive</span></a> <a href="https://fosstodon.org/tags/modeling" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>modeling</span></a> with examples in <a href="https://fosstodon.org/tags/R" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>R</span></a>. </p><p><a href="https://fosstodon.org/tags/datascience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>datascience</span></a> <a href="https://fosstodon.org/tags/dataanalysis" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>dataanalysis</span></a> <a href="https://fosstodon.org/tags/machinelearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>machinelearning</span></a> <a href="https://fosstodon.org/tags/rstats" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>rstats</span></a></p><p><span class="h-card"><a href="https://a.gup.pe/u/rstats" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>rstats</span></a></span></p><p><a href="https://dataalltheway.com/posts/013-linear-regression/" rel="nofollow noopener" target="_blank"><span class="invisible">https://</span><span class="ellipsis">dataalltheway.com/posts/013-li</span><span class="invisible">near-regression/</span></a></p>
Rohit<p><a href="https://mindly.social/tags/3goodthings" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>3goodthings</span></a> </p><p>1. Published the first draft of my latest blog post. It's a tutorial on <a href="https://mindly.social/tags/linearregression" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>linearregression</span></a>.</p><p>2. Made some progress on learning how to analyze Somascan data. </p><p>3. Went for a swim and sauna session. </p><p><span class="h-card"><a href="https://a.gup.pe/u/3goodthings" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>3goodthings</span></a></span></p>
Rohit Farmer, Ph.D.<p>I am looking for a real-life and recent dataset that I can use in a linear regression <a href="https://fosstodon.org/tags/tutorial" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>tutorial</span></a> for my blog. I am not interested in generic textbook examples like Boston Housing or Titanic etc. Any suggestion would be helpful. Thanks! <a href="https://fosstodon.org/tags/linearregression" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>linearregression</span></a> <a href="https://fosstodon.org/tags/blogpost" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>blogpost</span></a> <a href="https://fosstodon.org/tags/blogging" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>blogging</span></a> <a href="https://fosstodon.org/tags/datascience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>datascience</span></a> <a href="https://fosstodon.org/tags/machinelearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>machinelearning</span></a></p>
Cees Grootes<p>The difference between linear regression and correlation analysis.</p><p>How to do linear regression and correlation analysis.</p><p>Steps, methods, tools, and use cases for locating predictable user actions and improving retention.</p><p><a href="https://www.lennysnewsletter.com/p/linear-regression-and-correlation-analysis" rel="nofollow noopener" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">lennysnewsletter.com/p/linear-</span><span class="invisible">regression-and-correlation-analysis</span></a></p><p><a href="https://mastodon.social/tags/statistics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>statistics</span></a> <a href="https://mastodon.social/tags/correlation" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>correlation</span></a> <a href="https://mastodon.social/tags/correlationcoefficient" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>correlationcoefficient</span></a> <a href="https://mastodon.social/tags/regression" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>regression</span></a> <a href="https://mastodon.social/tags/linearregression" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>linearregression</span></a> <a href="https://mastodon.social/tags/prediction" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>prediction</span></a> <a href="https://mastodon.social/tags/sociology" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>sociology</span></a> <a href="https://mastodon.social/tags/psychology" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>psychology</span></a> <span class="h-card"><a href="https://a.gup.pe/u/sociology" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>sociology</span></a></span></p>
Samin Aref<p>📢 Are you interested in <a href="https://mas.to/tags/MachineLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>MachineLearning</span></a> 🤖 and <a href="https://mas.to/tags/Python" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Python</span></a> 🐍 but do not know where to start? You can check out my free course on YouTube▶️. I plan to release new recorded lectures and Python labs every week. <br>Please repost 🔃 </p><p>Lecture 3 - The connection between <a href="https://mas.to/tags/LinearRegression" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>LinearRegression</span></a>, Hypothesis Testing, Confidence Intervals, and Correlation<br><a href="https://www.youtube.com/watch?v=-PEoLbqgnQ8" rel="nofollow noopener" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">youtube.com/watch?v=-PEoLbqgnQ</span><span class="invisible">8</span></a></p><p>Link to the complete course playlist on YouTube<br>Data Science Methods and Statistical Learning<br><a href="https://www.youtube.com/playlist?list=PLSkGXOii6-CRlwmik1l1h9pG4Uuq0TgeT" rel="nofollow noopener" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">youtube.com/playlist?list=PLSk</span><span class="invisible">GXOii6-CRlwmik1l1h9pG4Uuq0TgeT</span></a></p>
Cees Grootes<p>Comparing Linear and Logistic Regression.</p><p>Discussion on an entry level data science interview question.</p><p>(by Devesh Rajadhyax | Nov, 2022 | Towards Data Science)</p><p><a href="https://towardsdatascience.com/comparing-linear-and-logistic-regression-11a3e1812212" rel="nofollow noopener" target="_blank"><span class="invisible">https://</span><span class="ellipsis">towardsdatascience.com/compari</span><span class="invisible">ng-linear-and-logistic-regression-11a3e1812212</span></a></p><p><a href="https://mastodon.social/tags/statistics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>statistics</span></a> <a href="https://mastodon.social/tags/research" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>research</span></a> <a href="https://mastodon.social/tags/data" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>data</span></a> <a href="https://mastodon.social/tags/datascience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>datascience</span></a> <a href="https://mastodon.social/tags/dataanalytics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>dataanalytics</span></a> <a href="https://mastodon.social/tags/linreg" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>linreg</span></a> <a href="https://mastodon.social/tags/logreg" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>logreg</span></a> <a href="https://mastodon.social/tags/linearregression" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>linearregression</span></a> <a href="https://mastodon.social/tags/logisticregression" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>logisticregression</span></a> <a href="https://mastodon.social/tags/dataresearch" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>dataresearch</span></a> <a href="https://mastodon.social/tags/sociology" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>sociology</span></a> <a href="https://mastodon.social/tags/psychology" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>psychology</span></a> <span class="h-card"><a href="https://a.gup.pe/u/sociology" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>sociology</span></a></span></p>
TrendingLive feeds
About