Through incremental integration and independent research and development, build a method library of big data quality control, automatic modeling and analysis, data mining and interactive visualization, form a tool library with high reliability, high scalability, high efficiency and high fault tolerance, realize the integration and sharing of collaborative analysis methods of multi-source heterogeneous, multi-granularity, multi-phase, long-time series big data in three pole environment, as well as high Efficient and online big data analysis and processing.
Gradient boosting machine is a machine learning technique for regression and classification problems, which produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees.
Installation: online;
Dependent libraries: sklearn;
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2019-10-20 869 View Details
The K-means algorithm identifies k number of centroids, and then allocates every data point to the nearest cluster, while keeping the centroids as small as possible.
Installation: online;
Dependent libraries: sklearn;
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2019-10-16 507 View Details
The nearest neighbors algorithm (k-NN) is a non-parametric method used for classification and regression. In both cases, the input consists of the k closest training examples in the feature space.
Installation: online;
Dependent libraries: sklearn;
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2019-10-15 236 View Details
Logistic regression is a statistical model that in its basic form uses a logistic function to model a binary dependent variable.
Installation: online;
Dependent libraries: sklearn;
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2019-10-14 1264 View Details
Naive Bayes is a simple technique for constructing classifiers: models that assign class labels to problem instances, represented as vectors of feature values, where the class labels are drawn from some finite set. There is not a single algorithm for training such classifiers, but a family of algorithms.
Installation: online;
Dependent libraries: sklearn;
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2019-10-15 648 View Details
A restricted Boltzmann machine (RBM) is a generative stochastic artificial neural network that can learn a probability distribution over its set of inputs.
Installation: online;
Dependent libraries: sklearn;
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2019-10-16 437 View Details
Apriori is an algorithm for frequent item set mining and association rule learning over relational databases. It proceeds by identifying the frequent individual items in the database and extending them to larger and larger item sets as long as those item sets appear sufficiently often in the database.
Installation: online;
Dependent libraries: sklearn;
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2019-10-15 367 View Details
A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data (supervised learning), the algorithm outputs an optimal hyperplane which categorizes new examples.
Installation: online;
Dependent libraries: sklearn;
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2019-10-14 589 View Details
Random forests or random decision forests are an ensemble learning method for classification, regression and other tasks that operates by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual tree.
Installation: online;
Dependent libraries: sklearn;
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2019-10-17 798 View Details
Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables (entities each of which takes on various numerical values) into a set of values of linearly uncorrelated variables called principal components.
Installation: online;
Dependent libraries: sklearn;
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2019-10-15 464 View Details
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