scikit learn and tensorflow book

Last year in Beijing participated in a big data conference organized by O ' Reilly and Cloudera, Strata , and was fortunate to have the O ' Reilly published hands-on machine learning with Scikit-learn and TensorFlow English book, in general, this is a good technical book, a

steps included in the text preprocessing process are summarized as follows:(1) cut a dime;(2) Throw away words that appear too frequent and do not help to match related documents;(3) Throw away the words that appear very low frequency, only very small may appear in the future post;(4) To count the remaining words;(5) Consider the whole expected set and calculate the TF-IDF value from the word frequency statistic.Through this process, we convert a bunch of noisy text into a concise feature repre

Feature index:\t ', bestfeatureindex print ' Best Threshold: \t\t ', Bestthreshold ' return {' Dim ': Bestfeatureindex, ' thresh ': bestthreshold, ' accuracy ': bestaccuracy}def apply_mo Del (features,labels,model): prediction = (features[:,model[' Dim ')] > model[' thresh ']) return prediction#--------- --cross validation-------------error = 0.0for ei in range (len (irisfeatures)): # Select All and the one at position ' ei ' : training = Np.ones (len (irisfeatures), bool) Training[ei] = False

eigenvectors matrix with 700 rows and 3000 columns. Each of these lines represents each message in the training set for 700 messages, and each column represents 3,000 keywords in the dictionary. The value on the "IJ" position represents the number of times that the word "J" in the dictionary appears in the message (letter i).def extract_features (Mail_dir):Files = [Os.path.join (MAIL_DIR,FI) for fi in Os.listdir (Mail_dir) www.dajinnylee.cn]Features_matrix = Np.zeros (len (Files), 3000))DocID =

Because of the recent intention to learn "machine learning combat" this book, so using Python may be used NumPy, matplotlib, scikit-learn These libraries, so the Internet to find how to install these libraries, look at a number of methods, after trying to find themselves very lucky, Soon it's done, and it's not complic

of higher-order polynomial curve, but this method of fitting can better obtain the development trend of data. In contrast to the over-fitting phenomenon of high-order polynomial curves, for low-order curves, there is no good description of the data, which leads to the case of less-fitting. So in order to better describe the characteristics of the data, using the 2-order curve to fit the data to avoid the occurrence of overfitting and under-fitting phenomenon.Training and testingWe trained to ge