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DictVectorizer — scikit-learn 1.5.0 documentation
on import DictVectorizer >>> v = DictVectorizer ( sparse = False...'foo' : 3 , 'baz' : 1 }] >>> X = v . fit_transform ( D ) >>> X array([[2.,...scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.DictVectorizer.html -
6.2. Feature extraction — scikit-learn 1.5.0 do...
norm: \(v_{norm} = \frac{v}{||v||_2} = \frac{v}{\sqrt{v{_1}^2 +...norm: \(v_{norm} = \frac{v}{||v||_2} = \frac{v}{\sqrt{v{_1}^2 +...scikit-learn.org/stable/modules/feature_extraction.html -
gtm.js
v=0,w=0;v<b.length-2;v+=3){var y=b[v], x=b[v+1],B=b[v+2],...0;y=v!==P.g.la?d(v):tm(c,v);if(dE.hasOwnProperty(v))MD(dE[v],v,y,e);else...www.googletagmanager.com/gtm.js -
MiniBatchDictionaryLearning — scikit-learn 1.5....
V ^* ) = argmin 0.5 || X - U V || _Fro ^ 2 + alpha...* || U || _1 , 1 ( U , V ) with || V_k || _2 <= 1 for all 0 <=...scikit-learn.org/stable/modules/generated/sklearn.decomposition.MiniBatchDictionaryLearning.html -
2.3. Clustering — scikit-learn 1.5.0 documentation
Likewise for \(V\) : \[H(V) = - \sum_{j=1}^{|V|}P'(j)\log(P'(j))\]...\frac{|U_i \cap V_j|}{N}\log\left(\frac{N|U_i \cap V_j|}{|U_i||V_j|}\right)\]...scikit-learn.org/stable/modules/clustering.html -
Slashdot: News for nerds, stuff that matters
computer with a quad-core RISC-V processor : it will be available...features: - Quad core 64bit RISC-V - HDMI video output - 4x DSI and...build.slashdot.org -
completeness_score — scikit-learn 1.5.0 documen...
v_measure_score V-Measure (NMI with arithmetic...Rosenberg and Julia Hirschberg, 2007. V-Measure: A conditional entropy-based...scikit-learn.org/stable/modules/generated/sklearn.metrics.completeness_score.html -
Bunch — scikit-learn 1.5.0 documentation
v in E: D[k] = v In either case, this is...scikit-learn.org/stable/modules/generated/sklearn.utils.Bunch.html -
homogeneity_score — scikit-learn 1.5.0 document...
v_measure_score V-Measure (NMI with arithmetic...Rosenberg and Julia Hirschberg, 2007. V-Measure: A conditional entropy-based...scikit-learn.org/stable/modules/generated/sklearn.metrics.homogeneity_score.html -
TransformedTargetRegressor — scikit-learn 1.5.0...
is defined as \((1 - \frac{u}{v})\) , where \(u\) is the residual...((y_true - y_pred)** 2).sum() and \(v\) is the total sum of squares...scikit-learn.org/stable/modules/generated/sklearn.compose.TransformedTargetRegressor.html