Smile – Statistical Machine Intelligence and Learning Engine

Print Friendly, PDF & Email
17 Views

Satu kata buat library ini yaitu “gila” yup dengan core engine berbasis java, maka kalian bisa pakai scala, kotlin, dan clojure, serta bahasa java itu sendiri. Kalau saya sih mending pakai kotlin saja! Smile merupakan library yang sangat lengkap sekali! Apa saja itu

  1. Classification : Decision Trees, AdaBoost, Gradient Boosting, Random Forest, Logistic Regression, Neural Networks, Support Vector Machines, RBF Networks, Maximum Entropy Classifier, Generic Naïve Bayes Classifier, Naïve Bayes Document Classfier, Fisher / Linear / Quadratic / Regularized Discriminant Analysis, Platt Scaling, Isotonic Regression Scaling, One vs. One, One vs. Rest
  2. Regression: Linear Regression, LASSO, ElasticNet, Ridge Regression, Regression Trees, Gradient Boosting, Random Forest, RBF Networks, Neural Networks, Support Vector Regression, Gaussian Process
  3. Feature Engineering and Selection: Bag of Words, Sparse One Hot Encoding, Standardizer, Robust Standardizer, Maximum Absolute Value Scaler, Winsor Scaler, Normalizer, Genetic Algorithm based Feature Selection, Ensemble Learning based Feature Selection, TreeSHAP, Signal Noise ratio, Sum Squares ratio
  4. Dimension Reduction: PCA, Kernel PCA, Probabilistic PCA, Generalized Hebbian Algorithm, Random Project, ICA
  5. Model Validation: Cross Validation, Leave-One-Out Validation, Bootstrap, Confusion Matrix, Accuracy, Error, AUC, Fallout, FDR, F-Score, Precision, Recall, Sensitivity, Specificity, Matthews Correlation Coefficient, MSE, RMSE, RSS, Mean Absolute Deviation, Rand Index, Adjusted Rand Index, Mutual Information Score, Hyperparameter Tuning
  6. Clustering: Hierarchical Clustering, CLARANS, DBSCAN, DENCLUE, K-Means, X-Means, G-Means, K-Modes, Deterministic Annealing, Sequential Information Bottleneck, Spectral Clustering, Minimum Entropy Clustering
  7. Vector Quantization:BIRCH, Self-Organizing Maps, Neural Gas, Growing Neural Gas, Neural Map
  8. Association Rules: Frequent Itemset Mining, Association Rule Mining
  9. Manifold learning: IsoMap, LLE, Laplacian Eigenmap, t-SNE, UMAP
  10. Multi-Dimensional Scaling: Classical MDS, Isotonic MDS, Sammon Mapping
  11. Nearest Neighbor Search: Linear Search, BK-Tree, Cover Tree, KD-Tree, LSH, Multi-Probe LSH, SimHash
  12. Sequence Learning: Hidden Markov Model, Conditional Random Field
  13. Time Series: ACF, PACF, Box-Pierce and Ljung-Box Test, AR, ARMA
  14. Natural Language Processing: Sentence Splitter, Tokenizer, Bigram Extractor, Phrase Extractor, Keyword Extractor, Porter Stemmer, Lancaster Stemmer, POS Tagging, Relevance Ranking, Word2Vec
  15. Linear Algebra: Dense Matrix, Band Matrix, Sparse Matrix, LU, Cholesky, QR, EVD, SVD, Computer Algebra System
  16. Statistics: Distributions, Random Number Generators, t-test, F-test, Χ2-test, Correlation Test, Kolmogorov-Smirnov Test
  17. Interpolation: Linear, Bilinear, Cubic, Bicubic, Kriging, Laplace, Shepard, RBF
  18. Wavelet: Discrete Wavelet Transform, Wavelet Shrinkage Haar Daubechies D4 Best Localized Wavelet, Coiflet, Symlet

Kecepatan

Dengan struktur dan algoritme data yang canggih, Smile menghadirkan kinerja yang canggih.

Kemudahan penggunaan

Tulis aplikasi dengan cepat dalam Java, Scala, atau bahasa JVM lainnya. Ilmuwan dan pengembang data menggunakan bahasa yang sama sekarang!

Komprehensif

Machine learning terlengkap. Simle mencakup setiap aspek machine learning! dengan 18 domain

Natural Language Processing

Memahami bahasa manusia, dan maksud di balik kata-kata kita. Tokenizer, stemming, word2vec, deteksi frase, penandaan part-of-speech, ekstraksi kata kunci, pengenalan entitas, analisis sentimen, peringkat relevansi, taksomi.

Matematika dan Statistik

Dari fungsi khusus, aljabar linier, hingga generator bilangan acak, distribusi statistik, dan uji hipotesis, Smile menyediakan lingkungan komputasi numerik yang canggih. Sebagai tambahan, grafik, wavlet, dan berbagai algoritma interpolasi diimplementasikan. Smile bahkan menyertakan sistem algerbra komputer.

Data Visualization

Scatter plot, line plot, tangga plot, bar plot, box plot, heatmap, hexmap, histogram, qq plot, permukaan, grid, kontur, dendrogram, visualisasi matriks jarang, wireframe, dll. Smile juga mendukung visualisasi data deklaratif yang dikompilasi ke Vega-Lite.

Dengan begitu banyak algoritma yang super lengkap! layak kalian pelajari, yuk mari https://haifengl.github.io/

 

Leave a Reply

Your email address will not be published. Required fields are marked *

88 + = 98