Pushing the docs to dev/ for branch: master, commit 613a3338f37a32577573055fdb5178359b50e9ce

Author: sklearn-ci

dev/_downloads/22b5d928415782a90ca8864871748096/plot_sparse_logistic_regression_mnist.py

@@ -1,6 +1,6 @@
 """
 =====================================================
-MNIST classfification using multinomial logistic + L1
+MNIST classification using multinomial logistic + L1
 =====================================================
 
 Here we fit a multinomial logistic regression with L1 penalty on a subset of

dev/_downloads/3409d9766d352cc9f9b169d4a799a87a/auto_examples_python.zip

@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n=====================================================\nMNIST classfification using multinomial logistic + L1\n=====================================================\n\nHere we fit a multinomial logistic regression with L1 penalty on a subset of\nthe MNIST digits classification task. We use the SAGA algorithm for this\npurpose: this a solver that is fast when the number of samples is significantly\nlarger than the number of features and is able to finely optimize non-smooth\nobjective functions which is the case with the l1-penalty. Test accuracy\nreaches > 0.8, while weight vectors remains *sparse* and therefore more easily\n*interpretable*.\n\nNote that this accuracy of this l1-penalized linear model is significantly\nbelow what can be reached by an l2-penalized linear model or a non-linear\nmulti-layer perceptron model on this dataset.\n"
+        "\n=====================================================\nMNIST classification using multinomial logistic + L1\n=====================================================\n\nHere we fit a multinomial logistic regression with L1 penalty on a subset of\nthe MNIST digits classification task. We use the SAGA algorithm for this\npurpose: this a solver that is fast when the number of samples is significantly\nlarger than the number of features and is able to finely optimize non-smooth\nobjective functions which is the case with the l1-penalty. Test accuracy\nreaches > 0.8, while weight vectors remains *sparse* and therefore more easily\n*interpretable*.\n\nNote that this accuracy of this l1-penalized linear model is significantly\nbelow what can be reached by an l2-penalized linear model or a non-linear\nmulti-layer perceptron model on this dataset.\n"
       ]
     },
     {