query: Statistics book for Machine Learning
positive_passages.docid: 18472
positive_passages.text: Machine learning cookbook / reference card / cheatsheet?
negative_passages.docid: 123766
negative_passages.text: Good examples/books/resources to learn about applied machine learning (not just ML itself)
negative_passages.docid: 7193
negative_passages.text: Textbooks on linear regression with least squares
negative_passages.docid: 123307
negative_passages.text: Ideas for outputting a prediction equation for Random Forests
negative_passages.docid: 18136
negative_passages.text: Mathematica learning resource dealing with bayesian inference
negative_passages.docid: 64175
negative_passages.text: How to estimate confidence level for SVM or Random Forest?
negative_passages.docid: 27710
negative_passages.text: What are the best books to study Neural Networks from a purely mathematical perspective?
negative_passages.docid: 50358
negative_passages.text: Choosing Random Forests' parameters
negative_passages.docid: 27388
negative_passages.text: scikit's RBM pseudo likelihood calculation
negative_passages.docid: 45881
negative_passages.text: Methods to work around the problem of missing data in machine learning
negative_passages.docid: 104508
negative_passages.text: Sources for learning (not just running) statistics/math through R
negative_passages.docid: 118434
negative_passages.text: Lift measure in data mining
negative_passages.docid: 73139
negative_passages.text: Flexible and inflexible models in machine learning
negative_passages.docid: 69831
negative_passages.text: Obtaining knowledge from a random forest
negative_passages.docid: 13161
negative_passages.text: What is the best introductory Bayesian statistics textbook?
negative_passages.docid: 146724
negative_passages.text: What exactly is a hypothesis space in machine learning?
negative_passages.docid: 155713
negative_passages.text: Book recommendation for analysis problems