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</span></code></pre></div><hr><p>Binary classification cost info is expressed in two terms: - cost of false positive - cost of false negative</p><hr><h2id=categoricalsegmentrule>CategoricalSegmentRule<aclass=headerlinkhref=#categoricalsegmentruletitle="Permanent link">¶</a></h2><divclass="language-python highlight"><pre><span></span><code><spanid=__span-10-1><aid=__codelineno-10-1name=__codelineno-10-1href=#__codelineno-10-1></a><spanclass=n>CategoricalSegmentRule</span><spanclass=p>()</span>
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</span></code></pre></div><hr><p>Rule for a segment over categorical values. It contains a list of values that are considered in <code>OR</code> logic to define the rule. See <code>SegmentRule</code> for additional details.</p><p><strong>Attributes</strong></p><ul><li><strong>values</strong> : list[str | int]</li></ul><p><strong>Methods:</strong></p><h3id=get_supported_data_types>.get_supported_data_types<aclass=headerlinkhref=#get_supported_data_typestitle="Permanent link">¶</a></h3><divclass="language-python highlight"><pre><span></span><code><spanid=__span-11-1><aid=__codelineno-11-1name=__codelineno-11-1href=#__codelineno-11-1></a><spanclass=o>.</span><spanclass=n>get_supported_data_types</span><spanclass=p>()</span>
</span></code></pre></div><hr><p>Column base model</p><p><strong>Attributes</strong></p><ul><li><strong>name</strong> : str</li><li><strong>role</strong> : ColumnRole</li><li><strong>is_nullable</strong> : bool</li><li><strong>data_type</strong> : DataType</li><li><strong>predicted_target</strong> : Optional[str] = None</li><li><strong>possible_values</strong> : Optional[list[str | int | bool]] = None</li><li><strong>model_id</strong> : Optional[str] = None</li><li><strong>dims</strong> : Optional[tuple[int]] = None it is mandatory when data_type is Array</li><li><strong>tol</strong> : Optional[int] = 0 Tolerance for ImageData width and height. Images can be loaded with size (w +- tol, h +- tol) pixels</li><li><strong>classes_names</strong> : Optional[list[str]] = None it is mandatory when the column is the target in multilabel classification tasks</li><li><strong>subrole</strong> : Optional[ColumnSubRole] = None Indicates the subrole of the column. It's used in RAG tasks to define the role of the input columns (e.g. user input or retrieved context)</li><li><strong>image_mode</strong> : Optional[ImageMode] = None Indicates the mode of the image. It must be provided when the data type is an image</li></ul><hr><h2id=company>Company<aclass=headerlinkhref=#companytitle="Permanent link">¶</a></h2><divclass="language-python highlight"><pre><span></span><code><spanid=__span-13-1><aid=__codelineno-13-1name=__codelineno-13-1href=#__codelineno-13-1></a><spanclass=n>Company</span><spanclass=p>()</span>
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</span></code></pre></div><hr><p>Column base model</p><p><strong>Attributes</strong></p><ul><li><strong>name</strong> : str</li><li><strong>role</strong> : ColumnRole</li><li><strong>is_nullable</strong> : bool</li><li><strong>data_type</strong> : DataType</li><li><strong>predicted_target</strong> : Optional[str] = None</li><li><strong>possible_values</strong> : Optional[list[str | int | bool]] = None</li><li><strong>model_id</strong> : Optional[str] = None</li><li><strong>dims</strong> : Optional[tuple[int]] = None it is mandatory when data_type is Array</li><li><strong>tol</strong> : Optional[int | None] = 0 Tolerance for ImageData width and height. Images can be loaded with size (w ± tol, h ± tol) pixels</li><li><strong>classes_names</strong> : Optional[list[str]] = None it is mandatory when the column is the target in multilabel classification tasks</li><li><strong>subrole</strong> : Optional[ColumnSubRole] = None Indicates the subrole of the column. It's used in RAG tasks to define the role of the input columns (e.g. user input or retrieved context)</li><li><strong>image_mode</strong> : Optional[ImageMode] = None Indicates the mode of the image. It must be provided when the data type is an image</li></ul><hr><h2id=company>Company<aclass=headerlinkhref=#companytitle="Permanent link">¶</a></h2><divclass="language-python highlight"><pre><span></span><code><spanid=__span-13-1><aid=__codelineno-13-1name=__codelineno-13-1href=#__codelineno-13-1></a><spanclass=n>Company</span><spanclass=p>()</span>
</span></code></pre></div><hr><p>base model for company user</p><p><strong>Attributes</strong></p><ul><li><strong>user_id</strong> : str</li><li><strong>company_role</strong> : UserCompanyRole</li></ul><hr><h2id=data>Data<aclass=headerlinkhref=#datatitle="Permanent link">¶</a></h2><divclass="language-python highlight"><pre><span></span><code><spanid=__span-15-1><aid=__codelineno-15-1name=__codelineno-15-1href=#__codelineno-15-1></a><spanclass=n>Data</span><spanclass=p>()</span>
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</span></code></pre></div><hr><p>Generic data model that contains all information about a data</p><p><strong>Attributes</strong></p><ul><li><strong>data_structure</strong> : DataStructure</li><li><strong>source</strong> : DataSource</li></ul><hr><h2id=databatch>DataBatch<aclass=headerlinkhref=#databatchtitle="Permanent link">¶</a></h2><divclass="language-python highlight"><pre><span></span><code><spanid=__span-16-1><aid=__codelineno-16-1name=__codelineno-16-1href=#__codelineno-16-1></a><spanclass=n>DataBatch</span><spanclass=p>()</span>
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