Search "+Peter Norvig +Automated machine learning +Class imbalance problem +Travelling salesman problem -Probability, Paradox, and the Reasonable Person Principle +Cross validation +Cluster analysis +Machine learning -Poker +Jupyter/notebook +Regex +scikit-learn +tfidf +Lexical feature selection -Artemy Kolchinsky -Text analysis"

Positive matches

• 0.098 + - Class imbalance
• 0.085 + - tf-idf
• 0.069 + - Regular expression
• 0.022 + - Ulrike von Luxburg
• 0.022 + - Data clustering
• 0.022 + - Decision tree
• 0.022 + - IPython notebook
• 0.021 + - Gradient descent
• 0.016 + - Recommender system
• 0.015 + - Natural language processing
• 0.015 + - Pointwise mutual information
• 0.014 + - Random forest
• 0.013 + - Python/Modules
• 0.013 + - IPython
• 0.013 + - Boosting (machine learning)
• 0.013 + - Python
• 0.012 + - Jupyter
• 0.012 + - skorch
• 0.010 + - Tim Hopper
• 0.010 + - Future of work

Negative matches

• 0.021 + - Science
• 0.012 + - Data analysis
• 0.009 + - Interdisciplinary research
• 0.007 + - Industry-Academic Partnership
• 0.003 + - Probability
• 0.002 + - Science policy
• 0.001 + - Collaborative software
• 0.001 + - Policy
• 0.001 + - Software engineering in research
• 0.001 + - Sentiment analysis
• 0.001 + - String metric
• 0.001 + - fastText
• 0.001 + - Word segmentation
• 0.001 + - Home
• 0.000 + - Gibbs' inequality
• 0.000 + - Sentence embedding
• 0.000 + - Latent Dirichlet allocation