E-commercetext features first and then apply the tf-idf algorithm The concatenation of all text columns in a single instance becomes the document, and the set of all these instances becomes the corpus). The second approach was to apply the tf-idf algorithm separately to C Level Executive List each feature Each individual column is a corpus) and then concatenate the resulting arrays. The resulting table after tf-idf is very sparse (most columns for a given instance are null), C Level Executive List so we applied dimensionality reduction (single value decomposition) to reduce the number of attributes/columns. The last step was to concatenate all the resulting columns from
All the entity categories into an array. We did this after applying all the steps above (cleaning up features, turning categorical features into labels and performing hot encoding on the labels, applying tf-idf to text features and updating scaling of all features to center them C Level Executive List around the mean). Models and sets after getting and concatenating all the features, we ran a number of different algorithms on it. The algorithms that have shown the most promise are C Level Executive List the gradient boost classifier, the ridge classifier, and a two-layer neural network. Finally, we collated the model results using simple averages, and therefore saw additional gains, as
Different models tend to have different biases. Optimize threshold the final step was to decide on a threshold for turning the probability estimates into binary predictions ("Yes, we predict this site will be in google's top 10" or "No, we predict this site will not google's top 10"). For this, we optimized a cross-validation set and then used the threshold obtained on a C Level Executive List C Level Executive List test set. Results the metric that we thought was the most representative to measure the efficiency of the model is a confusion matrix. A confusion matrix is a chart that is often used to describe the performance of a classification model (or "Classifier") on a set of test data for which the true values are known. I'm sure you've heard the saying that “a broken clock is right twice