Binary classification notebook. Develop a model that uses various network features to detect which network activities are part of an intrusion/attack. Binary Classification. Medical Diagnosis (ex : whether a patient has cancer or not) The encoder will encode “M” as 1, “F” as 2, and “U” as 3 according to the magnitude of their average opioid abuse. variance). Classification trees (Yes/No types) : What we’ve seen above is an example of classification tree, where the outcome was a variable like ‘fit’ or ‘unfit’. df.drop(['Neurology Payment Flag', 'Neurosurgery Payment Flag', 'Dentist Payment Flag'. Then, one of the k neighbors is randomly selected and a synthetic sample is built from a randomly selected point between the original observation and the randomly selected neighbor. Often students make the mistake of applying the first step which ultimately changes the structure of the data or the name of the feature which is not recognized by the second stop. ClaimID Unique: Identifier for a claimAccident DateID: Number of days since the accident occurred from an arbitrary dateClaim Setup DateID: Number of days since the Resolution Manager sets up the claim from an arbitrary dateReport To DateID: Number of days since the employer notifies insurance of a claim from an arbitrary dateEmployer Notification DateID: Number of days since the claimant notifies employer of an injury from an arbitrary dateBenefits State: The jurisdiction whose benefits are applied to a claimAccident State: State in which the accident occurredIndustry ID: Broad industry classification categoriesClaimant Age: Age of the injured worker Claimant Sex: Sex of the injured worker Claimant State: State in which the claimant residesClaimant Marital Status: Marital status of the injured worker Number Dependents: Number of dependents the claimant hasWeekly Wage: An average of the claimant’s weekly wages as of the injury date.Employment Status Flag: F — Regular full-time employee P — Part-time employee U — Unemployed S — On strike D — Disabled R — Retired O — Other L — Seasonal worker V — Volunteer worker A — Apprenticeship full-time B — Apprenticeship part-time C — Piece workerRTW Restriction Flag: A Y/N flag, used to indicate whether the employees responsibilities upon returning to work were limited as a result of his/her illness or injury.Max Medical Improvement DateID: DateID of Maximum Medical Improvement, after which further recovery from or lasting improvements to an injury or disease can no longer be anticipated based on reasonable medical probability.Percent Impairment: Indicates the percentage of anatomic or functional abnormality or loss, for the body as a whole, which resulted from the injury and exists after the date of maximum medical improvementPost Injury Weekly Wage: The weekly wage of the claimant after returning to work, post-injury, and/or the claim is closed.NCCI Job Code: A code that is established to identify and categorize jobs for workers’ compensation.Surgery Flag: Indicates if the claimant’s injury will require or did require surgeryDisability Status: — Temporary Total Disability (TTD) — Temporary Partial Disability (TPD) — Permanent Partial Disability (PPD) — Permanent Total Disability (PTD)SIC Group: Standard Industry Classification group for the clientNCCI BINatureOfLossDescription: Description of the end result of the bodily injury (BI) loss occurrenceAccident Source Code: A code identifying the object or source which inflicted the injury or damage.Accident Type Group: A code identifying the general action which occurred resulting in the lossNeurology Payment Flag: Indicates if there were any payments made for diagnosis and treatment of disorders of the nervous system without surgical interventionNeurosurgery Payment Flag: Indicates if there were any payments made for services by physicians specializing in the diagnosis and treatment of disorders of the nervous system, including surgical intervention if neededDentist Payment Flag: Indicates if there were any payments made for prevention, diagnosis, and treatment of diseases of the teeth and gumsOrthopedic Surgery Payment Flag: Indicates if there were any payments made for surgery dealing with the skeletal system and preservation and restoration of its articulations and structures.Psychiatry Payment Flag: Indicates if there were any payments made for treatment of mental, emotional, or behavioral disorders.Hand Surgery Payment Flag: Indicates if there were any payments made for surgery only addressing one or both hands.Optometrist Payment Flag: Indicates if there were any payments made to specialists who examine the eye for defects and faults of refraction and prescribe correctional lenses or exercises but not drugs or surgeryPodiatry Payment Flag: Indicates if there were any payments made for services from a specialist concerned with the care of the foot, including its anatomy, medical and surgical treatment, and its diseases.HCPCS A Codes — HCPCS Z Codes: Count of the number of HCPCS codes that appear on the claim within each respective code groupICD Group 1 — ICD Group 21: Count of the number of ICD codes that appear on the claim w/in each respective code groupCount of the number of codes on the claim — CPT Category — Anesthesia — CPT Category — Eval_Mgmt — CPT Category — Medicine — CPT Category — Path_Lab — CPT Category — Radiology — CPT Category — SurgeryCount of the number of NDC codes on the claim within each respective code class — NDC Class — Benzo — NDC Class — Misc (Zolpidem) — NDC Class — Muscle Relaxants — NDC Class — StimulantsOpioids Used: A True (1) or False (0) indicator for whether or not the claimant abused an opioid. In the next section, we’ll attempt hyperparameter tuning to see if we can increase the classification recall of the logistic regression model. Our dataset contains just over 16,000 observations along with 92 features including the target (ie. Let’s now turn our attention to cardinality or the number of unique values/categories for each feature. After each training, AI Builder uses the test data set to evaluate the quality and accuracy of the new model. Unfortunately, we did not increase our training recall but we were able to increase our test recall from 0.945 (log regression w/RF features and SMOTE) to 0.954. Notice in the code we have constructed a correlation matrix and converted the correlations to their absolute values in order to deal with negative correlations. The classifiers will not “see” any of the test data during training. The example problem is to predict if a banknote (think euro or dollar bill) is … Churn prediction (churn or not). Remember that K-fold cross-validation means splitting the training set into K folds, then making predictions and evaluating them on each fold using a model trained on the remaining folds: Wow! [9] Since many classification methods have been developed specifically for binary classification, multiclass classification often requires the combined use of multiple binary classifiers. Conversion prediction (buy or not). Binary classification notebook Open notebook in new tab Copy link for import Decision trees examples These examples demonstrate various applications of decision trees using the Apache Spark MLlib Pipelines API. Oversampling is a technique which attempts to add random copies of the minority class to the dataset until the imbalance is eliminated. As an example, the feature ‘Employment Status Flag’ currently has 13 categories (including np.nan) but as you can see the “F = Full-Time” and “P=Part-Time” categories make up almost 96% of the data. 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