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>
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#Lasso #LinearRegression "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"
https://scikit-learn.org/stable/modules/linear_model.html#lasso 
scikit-learn1.1. Linear ModelsThe following are a set of methods intended for regression in which the target value is expected to be a linear combination of the features. In mathematical notation, if\hat{y} is the predicted val...
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@data "practitioners can leverage #LASSO regression to construct more interpretable and predictive models that excel in scenarios involving high-dimensional data and intricate feature relationships."
https://datasciencedecoded.com/posts/12_LASSO_Regression_Feature_Selection_Predictive_Models
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How to identify and handle duplicate values: https://stackabuse.com/handling-duplicate-values-in-a-pandas-dataframe/
Stack Abuse · Handling Duplicate Values in a Pandas DataFrameAs a data analyst, it is our responsibility to ensure data integrity to obtain accurate and trustworthy insights. Data cleansing plays a vital role in this pro...
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A categorical variable takes on a limited number of values.
The categorical #dataType is useful in the following cases:
- A string variable consisting of only some values. df[["label"]].astype("category") saves memory.
- The lexical order is not the same as the logical order (“one”, “two”, “three”). Sorting and min/max will use the logical order.
- As a signal to other libraries to treat as a category.
More: https://pandas.pydata.org/pandas-docs/stable/user_guide/categorical.html
pandas.pydata.orgCategorical data — pandas 2.2.3 documentation
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"Extract Year from a datetime column", by Piyush Raj: https://datascienceparichay.com/article/pandas-extract-year-from-datetime-column/
Data Science Parichay · Pandas - Extract Year from a datetime column - Data Science ParichayTo extract the year from a datetime column in pandas, simply access it by referring to its "year" property. For example, df.['Col'].dt.year