data › Easy to use CatBoost advantages › Good quality for default parameters › Sophisticated categorical features support › Models analysis tools 44 / 62 Gradient Boosting 45 / 62 Gradient Boosting 46 / 62 Gradient Boosting 47 / 62 Gradient Boosting 48 / 62 Numerical features 49 / 62 Categorical features 50 / 62 Algorithm comparison 51 / 62 Prediction time 52 / 62 Applying CatBoost models https://github.com/clickhouse/ClickHouse 61 / 62 Thank you! QA 62 / 62 Symmetric trees 63 / 62 Categorical features support 64 / 62

data › Easy to use CatBoost advantages › Good quality for default parameters › Sophisticated categorical features support › Models analysis tools 44 / 62 Gradient Boosting 45 / 62 Gradient Boosting 46 / 62 Gradient Boosting 47 / 62 Gradient Boosting 48 / 62 Numerical features 49 / 62 Categorical features 50 / 62 Algorithm comparison 51 / 62 Prediction time 52 / 62 Applying CatBoost models https://github.com/clickhouse/ClickHouse 61 / 62 Thank you! QA 62 / 62 Symmetric trees 63 / 62 Categorical features support 64 / 62

(SVD) Norms and Distance FuncDons Sparse Vectors Principal Component Analysis (PCA) Encoding Categorical Variables Pivot Stemming Sept 2017 Graph All Pairs Shortest Path (APSP) Breadth-First Search

using methods in descriptive and inferential statistics, pivoting, sessionization and encoding categorical variables. There is also a comprehensive library of graph, supervised learning and unsupervised types and non-numeric, categorical, types. The training function has two signatures – one for the case where all attributes are numeric and another for mixed numeric and categorical types. Additional arguments - No 1 - Yes There are four attributes: outlook, temperature, humidity, and wind. These are categorical variables. The MADlib create_nb_classify_view() function expects the attributes to be VMware Greenplum

using methods in descriptive and inferential statistics, pivoting, sessionization and encoding categorical variables. There is also a comprehensive library of graph, supervised learning and unsupervised types and non-numeric, categorical, types. The training function has two signatures – one for the case where all attributes are numeric and another for mixed numeric and categorical types. Additional arguments 402 1 - Yes There are four attributes: outlook, temperature, humidity, and wind. These are categorical variables. The MADlib create_nb_classify_view() function expects the attributes to be provided

using methods in descriptive and inferential statistics, pivoting, sessionization and encoding categorical variables. There is also a comprehensive library of graph, supervised learning and unsupervised types and non-numeric, categorical, types. The training function has two signatures – one for the case where all attributes are numeric and another for mixed numeric and categorical types. Additional arguments - No 1 - Yes There are four attributes: outlook, temperature, humidity, and wind. These are categorical variables. The MADlib create_nb_classify_view() function expects the attributes to be VMware

using methods in descriptive and inferential statistics, pivoting, sessionization and encoding categorical variables. There is also a comprehensive library of graph, supervised learning and unsupervised types and non-numeric, categorical, types. The training function has two signatures – one for the case where all attributes are numeric and another for mixed numeric and categorical types. Additional arguments - No 1 - Yes There are four attributes: outlook, temperature, humidity, and wind. These are categorical variables. The MADlib create_nb_classify_view() function expects the attributes to be provided

using methods in descriptive and inferential statistics, pivoting, sessionization and encoding categorical variables. There is also a comprehensive library of graph, supervised learning and unsupervised types and non-numeric, categorical, types. The training function has two signatures – one for the case where all attributes are numeric and another for mixed numeric and categorical types. Additional arguments - No 1 - Yes There are four attributes: outlook, temperature, humidity, and wind. These are categorical variables. The MADlib create_nb_classify_view() function expects the attributes to be provided

using methods in descriptive and inferential statistics, pivoting, sessionization and encoding categorical variables. There is also a comprehensive library of graph, supervised learning and unsupervised types and non-numeric, categorical, types. The training function has two signatures – one for the case where all attributes are numeric and another for mixed numeric and categorical types. Additional arguments - No 1 - Yes There are four attributes: outlook, temperature, humidity, and wind. These are categorical variables. The MADlib create_nb_classify_view() function expects the attributes to be provided

using methods in descriptive and inferential statistics, pivoting, sessionization and encoding categorical variables. There is also a comprehensive library of graph, supervised learning and unsupervised types and non-numeric, categorical, types. The training function has two signatures – one for the case where all attributes are numeric and another for mixed numeric and categorical types. Additional arguments - No 1 - Yes There are four attributes: outlook, temperature, humidity, and wind. These are categorical variables. The MADlib create_nb_classify_view() function expects the attributes to be provided