machine learning buzzwords

The 2nd Chapter Perception MachineThe Perceptron is a linear classification model of class Two classification, whose input is the characteristic vector of an instance, and the perceptual machine corresponds to the separation of the examples into positive and negative two classes in the input space (feature space), which belongs to the discriminant model. A loss function is introduced based on the error classification, and the loss function is minimize

The shape function is a function in Numpy.core.fromnumeric, whose function is to read the length of the matrix, for example, Shape[0] is to read the length of the first dimension of the matrix. Its input parameters can make an integer representation of a dimension, or it can be a matrix.Use Shape to import numpyThe tile function is in the Python module numpy.lib.shape_base, and his function is to repeat an array. For example, Tile (a,n), function is to repeat the array a n times to form a new ar

=grid_search.best_estimator_.get_ params () for param_name in sorted (Parameters.keys ()): print (' \t%s:%r '% (Param_name,best_parameters[param_name]) )Output Result:Best results: 0.929 optimal parameters: clf__max_depth:250 clf__min_samples_leaf:1 clf__min_samples_split:3 clf__n_estimators:50Predictions=grid_search.predict (x_test) print Classification_report (y_test,predictions)Output Result:Precision Recall F1-score Support0 0.98 1.00) 0.99 7051 0.97 0.90) 0.93 115Avg/total 0.98 0.98 0.98

products, and so on, can be abstracted into vectors to allow the computer to know the distance between two properties. For example: We believe that 18-year-olds are closer to the 24-year-old than the 12-year-old, which is closer to the product than the computer, and so on.as long as the real-world objects can be abstracted into vectors, you can use the K-means algorithm to classify .In the "K-mean Clustering (K-means)" This article cited a very good application example, the author made a vector

nodes on the node on behalf of a variety of fractions, example to get the classification result of Class 1The same input is transferred to different nodes and the results are different because the respective nodes have different weights and biasThis is forward propagation.10. MarkovVideoMarkov Chains is made up of state and transitionsChestnuts, according to the phrase ' The quick brown fox jumps over the lazy dog ', to get Markov chainStep, set each word to a state, and then calculate the prob

train our models. Let's see what methods are available and what parameters are required as input. First we import the built-in library file als:import org.apache.spark.mllib.recommendation.ALSThe next operation is done in Spark-shell. Under Console, enter ALS. (Note that there is a point behind the ALS) plus the TAP key:The method we are going to use is the train method.If we enter Als.train, we will return an error, but we can look at the details of this method from this error:As you can see,

-validation approach. Cross-validation A simple idea to solve the above model selection problem is that I use 70% of the data to train each model, with 30% of the data for training error calculation, and then we compare the training errors of each model, we can choose the training error is relatively small model. If you do not refer to these errors (learn the theory of experience risk minimization--andrew ng machine

Liblinear instead of LIBSVM 2.Liblinear use, Java version Http://www.cnblogs.com/tec-vegetables/p/4046437.html 3.Liblinear use, official translation. http://blog.csdn.net/zouxy09/article/details/10947323/ http://blog.csdn.net/zouxy09/article/details/10947411 4. Here is an article, write good. Transferred from: http://blog.chinaunix.net/uid-20761674-id-4840097.html For the past more than 10 years, support vector machines (SVM machines) have been the most influential algorithms in

Transferred from: http://www.csdn.net/article/2015-10-16/2825925First of all, let me start with my intentions . Machine learning system now much more red NB this thing I don't have to repeat. But because of the particularity of machine learning system, it is not easy to build a reliable and useful system. Every time I

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