Statistics for Machine Learning: Techniques for exploring supervised, unsupervised, and reinforcement learning models with Python and RPackt Publishing Ltd, 21 Jul 2017 - 442 halaman Build Machine Learning models with a sound statistical understanding. Key Features
This book is intended for developers with little to no background in statistics, who want to implement Machine Learning in their systems. Some programming knowledge in R or Python will be useful. |
Isi
| 1 | |
| 7 | |
Parallelism of Statistics and Machine Learning | 55 |
Logistic Regression Versus Random Forest | 83 |
TreeBased Machine Learning Models | 125 |
KNearest Neighbors and Naive Bayes | 186 |
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Istilah dan frasa umum
Actuall"],colnames AdaBoost applied Bellman equations calculated Centroid clusters collaborative filtering computing Confusion Matrix Confusion Matrix Predicted convergence decision trees deep learning dimensions elif y[i Ensemble equation error example following code frac_tszero gradient boosting gradient descent Grid Search Hence import pandas input learning rate linear regression logistic regression machine learning models methodology Monte Carlo methods Naive Bayes neural networks neurons number of iterations numpy observations optimal policy output p-value pandas as pd parameters percent performance plot plt.show Polynomial Kernel precision recall principal component print(ts_tble problem Python R-squared random forest RBF Kernel recall f1-score support reinforcement learning reward sample scikit-learn similar spam statistical modeling Support vector Support vector machines table(ts_y_act,ts_y_pred test accuracy Test Classification Report Test Confusion Matrix test data tr_tble Train accuracy train and test Train Confusion training data trprec_zero ts_acc ts_y_pred unsupervised learning update users utilized value function variables variance explained weights whereas XGBoost