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gtm.js
a(c[d])&&(c[d]=[]),c[d]=k(e,c[d])):Va(e)?(Va(c[d])||(c[d]={}...Qc("",function(d,e){b&&(e=d,d=void 0);c&&(e=d);d instanceof Qc||(d=void...www.googletagmanager.com/gtm.js -
sklearn.utils.Bunch — scikit-learn 1.4.2 docume...
all items from D. copy ( ) → a shallow copy of D fromkeys ( iterable...method, then does: for k in E: D[k] = E[k] If E is present and...scikit-learn.org/stable/modules/generated/sklearn.utils.Bunch.html -
2.2. Manifold learning — scikit-learn 1.4.2 doc...
is \(O[D \log(k) N \log(N)] + O[D N k^3] + O[N d^6] + O[d N^2]\)...is \(O[D \log(k) N \log(N)] + O[D N k^3] + O[k^2 d] + O[d N^2]\)...scikit-learn.org/stable/modules/manifold.html -
GitHub - codelibs/fess-testdata: Test Data Repo...
on color-fg-success d-none m-2"> <path d="M13.78 4.22a.75.75...js-clipboard-check-icon color-fg-success d-none"> <path d="M13.78 4.22a.75.75 0...github.com/codelibs/fess-testdata -
1.8. Cross decomposition — scikit-learn 1.4.2 d...
\(X \in \mathbb{R}^{n \times d}\) and \(Y \in \mathbb{R}^{n \times...compute \(u_k \in \mathbb{R}^d\) and \(v_k \in \mathbb{R}^t\)...scikit-learn.org/stable/modules/cross_decomposition.html -
1.6. Nearest Neighbors — scikit-learn 1.4.2 doc...
samples in \(D\) dimensions, this approach scales as \(O[D N^2]\) ....approximately \(O[D \log(N)]\) KD tree query time changes with \(D\) in a...scikit-learn.org/stable/modules/neighbors.html -
sklearn.linear_model.GammaRegressor — scikit-le...
D^2 is defined as \(D^2 = 1-\frac{D(y_{true},y_{pred})}{D_{null}}\)...Compute D^2, the percentage of deviance explained. D^2 is a generalization...scikit-learn.org/stable/modules/generated/sklearn.linear_model.GammaRegressor.html -
Putting it all together — scikit-learn 1.4.2 do...
"n_samples: %d " % n_samples ) print ( "n_features: %d " % n_features..."Extracting the top %d eigenfaces from %d faces" % ( n_components...scikit-learn.org/stable/tutorial/statistical_inference/putting_together.html -
sklearn.linear_model.TweedieRegressor — scikit-...
D^2 is defined as \(D^2 = 1-\frac{D(y_{true},y_{pred})}{D_{null}}\)...Compute D^2, the percentage of deviance explained. D^2 is a generalization...scikit-learn.org/stable/modules/generated/sklearn.linear_model.TweedieRegressor.html -
sklearn.manifold.Isomap — scikit-learn 1.4.2 do...
frobenius_norm[K(D) - K(D_fit)] / n_samples Where D is the matrix...isomap kernel: K(D) = -0.5 * (I - 1/n_samples) * D^2 * (I - 1/n_samples)...scikit-learn.org/stable/modules/generated/sklearn.manifold.Isomap.html