Automation feature and most useful set of tools and options make it very convenient to understand and process the data. Moreover, you will get support by enhanced development features like hotkeys and auto-correction. It supports the famous tools like HTML, SQL, Java and textual data. You can also like Toad Data Modeler 6.4.įurthermore, you can perform code formatting and reviewing at its best due to available powerful set of features. You will experience an easy to understand working environment as it ensures fast learning during processing. You can better understand the database flow with virtual representation of database. It comes with special tools for development and ensures the enhanced productivity while performing data management. TOAD for Oracle 13.1 Xpert Edition allows you to manage Oracle database management system and handle HTML, java, SQL and other tools. TOAD for Oracle 13.1 is powerful application to manage oracle database. Setup file is completely standalone and also its an offline installer. Toad is dedicated to facilitating model development process, especially for a scorecard.TOAD for Oracle 13.1 Free Download for Windows supporting 32-bit and 64-bit architectures. It provides intuitive functions of the entire process, from EDA, feature engineering and selection etc. to results validation and scorecard transformation. Its key functionality streamlines the most critical and time-consuming process such as feature selection and fine binning. The following showcases some of the most popular features of toad, for more detailed demonstrations and user guidance, please refer to the tutorials.Simple IV calculation for all features.

quality ( data, 'target', iv_only = True ).Preliminary selection based on criteria.and stepwise feature selection (with optimised algorithm). Reliable fine binning with visualisation.stepwise ( data_woe, target = 'target', estimator = 'ols', direction = 'both', criterion = 'aic', exclude = to_drop ) select ( data, target = 'target', empty = 0.5, iv = 0.02, corr = 0.7, return_drop = True, exclude = ) final_data = toad. drop ( to_drop, axis = 1 ), y = 'target', method = 'chi', min_samples = 0.05 ) print ( c. Transform ( data_selected ], labels = True ), x = col, target = 'target' )Įxport ()) # Visualisation to check binning results col = 'feature_name' bin_plot ( c.

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