AIP-210 CertNexus Certified Artificial Intelligence Practitioner (CAIP) Questions and Answers

Lasso regression is a type of linear regression that adds a regularization term to the loss function to reduce overfitting and improve generalization. Lasso regression uses an L1 norm as the regularization term, which is the sum of the absolute values of the coefficients. Lasso regression can shrink some of the coefficients to zero, which effectively eliminates some of the features from the model. Lasso regression is preferable over ridge regression when there are many features with no association with the dependent variable, as it can perform feature selection and reduce the complexity and noise of the model.

SVM models can use kernel functions to map the input data into higher-dimensional feature spaces, where linear separation is possible. This allows SVM models to handle non-linear problems effectively. References: CertNexus Certified Artificial Intelligence Practitioner, Support vector machine - Wikipedia

The General Data Protection Regulation (GDPR) is a privacy-focused law that an AI practitioner should adhere to while designing and adapting an AI system that utilizes personal data. The GDPR applies to any organization that processes personal data of individuals in the European Union (EU), regardless of where the organization is located. The GDPR grants individuals rights over their personal data, such as the right to access, rectify, erase, restrict, or object to its processing. The GDPR also imposes obligations on organizations that process personal data, such as the duty to obtain consent, conduct data protection impact assessments, implement data protection by design and by default, and ensure accountability and transparency. The GDPR also addresses some specific issues related to AI, such as automated decision-making, profiling, and data portability.

The Beta value in an A/B test is the probability of making a type II error, which is failing to reject the null hypothesis when it is false. The statistical power of a test is the probability of correctly rejecting the null hypothesis when it is false, which is equal to 1 - Beta. References: Formulas for Bayesian A/B Testing – Evan Miller, The Practical Guide To AB testing statistics | Convertize

Sampling bias occurs when a data segment is collected in such a way that some members of the intended statistical population are less likely to be included than others. This can result in a sample that is not representative of the population and may lead to inaccurate or misleading conclusions. Sampling bias can be caused by various factors, such as non-random sampling methods, non-response, self-selection, or convenience sampling. References: [Sampling bias - Wikipedia], [What is Sampling Bias? Definition, Types and Examples]

Normalization is a method that can help us to solve the problem of features being on different scales when using the k-nearest neighbors algorithm. Normalization is a technique that rescales the values of features to a common range, such as [0, 1] or [-1, 1]. Normalization can help reduce the influence or dominance of some features over others, as well as improve the accuracy and performance of the algorithm2.

Personal data is any information that relates to an identified or identifiable individual, such as name, address, email, phone number, or biometric data. Personal data should not be disclosed, made available, or otherwise used for purposes other than specified, except with:

• The consent of the person it is collected from: Consent is a clear and voluntary indication of agreement by the person to the processing of their personal data for a specific purpose. Consent can be given by a statement or a clear affirmative action, such as ticking a box or clicking a button.
• The authority of law: The authority of law is a legal basis or obligation that requires or permits the processing of personal data for a legitimate purpose. For example, the authority of law could be a court order, a subpoena, a warrant, or a statute.

Random forest is an algorithm that is ideal to prevent overfitting when using a dataset with many features and a small sample size. Random forest is an ensemble learning method that combines multiple decision trees to create a more robust and accurate model. Random forest can prevent overfitting by introducing randomness and diversity into the model, such as by using bootstrap sampling (sampling with replacement) to create different subsets of data for each tree, or by using feature selection (choosing a random subset of features) to split each node in a tree.

IBM Watson is an AI technology that can take a question in natural language and return a precise answer to the question. IBM Watson is a cognitive computing system that can understand natural language, generate hypotheses, and provide evidence-based answers. IBM Watson can be applied to various domains and industries, such as healthcare, education, finance, or law.

Emergent bias is a type of bias that arises when an AI model encounters new or different data or scenarios that were not present or accounted for during its training or development. Emergent bias can cause the model to make inaccurate or unfair predictions or decisions, as it may not be able to generalize well to new situations or adapt to changing conditions. One possible cause of emergent bias is seasonality, which means that some variables or patterns in the data may vary depending on the time of year. For example, if an AI model for merchandise sales prediction was trained in winter and applied in summer, it may produce biased results due to differences in customer behavior, demand, or preferences.

When concept drift is detected in the pipeline, it means that the model performance has degraded over time due to changes in the underlying data generating process. This requires retraining the model with new data that reflects the current situation and updating the model parameters accordingly. References: Use pipeline parameters to retrain models in the designer - Azure Machine Learning | Microsoft Learn, Retraining Model During Deployment: Continuous Training and Continuous Testing

 Retraining is the process of updating an existing ML model with new or updated data to maintain or improve its performance and relevance. Retraining can help address various issues or challenges in ML systems, such as data drift, concept drift, model degradation, or changing requirements. Retraining can be done using different strategies, such as periodically, continuously, or on-demand.

For an AI system that recommends New Year’s resolutions, retraining periodically every year would be the best strategy for this pipeline. This is because New Year’s resolutions are seasonal and time-sensitive, meaning that they may vary depending on the year or the current situation. Retraining periodically every year can help ensure that the system’s recommendations are up-to-date and relevant for each new year.

Caffe is a deep learning framework that is designed for speed and modularity. It can process large datasets of images efficiently and supports various types of neural networks. It also has a large and active developer community that contributes to its code base and documentation. Caffe is suitable for image processing tasks such as classification, segmentation, detection, and recognition

Oversampling is a method that can help address the issue of imbalanced data, which is when one class is much more frequent than the other in the dataset. This can cause the model to be biased towards the majority class and have a low true negative rate. Oversampling involves creating synthetic samples of the minority class or replicating existing samples to balance the class distribution. This can help the model learn more from the minority class and improve the true negative rate. References: [Handling imbalanced datasets in machine learning], [Oversampling and undersampling in data analysis - Wikipedia]

A type 1 error in statistical hypothesis testing is when the null hypothesis is true, but is rejected. This means that the test falsely concludes that there is a significant difference or effect when there is none. The probability of making a type 1 error is denoted by alpha, which is also known as the significance level of the test. A type 1 error can be reduced by choosing a smaller alpha value, but this may increase the chance of making a type 2 error, which is when the null hypothesis is false but fails to be rejected. References: [Type I and type II errors - Wikipedia], [Type I Error and Type II Error - Statistics How To]

Encoding is a technique that transforms categorical data into numerical features that can be used by machine learning models. Categorical data are data that have a finite number of possible values or categories, such as gender, color, or country. Encoding can help convert categorical data into a format that is suitable and understandable for machine learning models. Some of the encoding methods that can be used to transform categorical data into numerical features are:

• Mean Encoder: Mean encoder is a method that replaces each category with the mean value of the target variable for that category. Mean encoder can capture the relationship between the category and the target variable, but it may cause overfitting or multicollinearity problems.
• One-Hot Encoder: One-hot encoder is a method that creates a binary vector for each category, where only one element has a value of 1 (the hot bit) and the rest have a value of 0. One-hot encoder can create distinct and orthogonal vectors for each category, but it may increase the dimensionality and sparsity of the data.

Missing values are a common problem in data analysis and machine learning, as they can affect the quality and reliability of the data and the model. There are various methods to deal with missing values, such as deleting, imputing, or ignoring them. One of the most common methods is imputing, which means replacing the missing values with some estimated values based on some criteria. For continuous variables, one of the simplest and most widely used imputation methods is to fill in the missing values with the mean (average) of the observed values for that variable in the entire dataset. This method can preserve the overall distribution and variance of the data, as well as avoid introducing bias or noise.

A handover is the process of transferring the ownership and responsibility of an ML system from one party to another, such as from the developers to the end users. A handover should include all the necessary information and resources that enable the end users to use and run a trained model on their own system. Some of the items that should be included in a handover are:

• Link to a GitHub repository of the codebase: A GitHub repository is an online platform that hosts the source code and version control of an ML system. A link to a GitHub repository can provide the end users with access to the latest and most updated version of the codebase, as well as the history and documentation of the changes made to the code.
• README document: A README document is a text file that provides an overview and instructions for an ML system. A README document can include information such as the purpose, features, requirements, installation, usage, testing, troubleshooting, and license of the system.
• Sample input and output data files: Sample input and output data files are data files that contain examples of valid inputs and expected outputs for an ML system. Sample input and output data files can help the end users understand how to use and run the system, as well as verify its functionality and performance.

Model validation is the process of checking whether the model meets the specified requirements and quality standards. It involves testing the model on a validation dataset, which is different from the training and testing datasets, and evaluating the model performance using appropriate metrics. References: Overview of ML Pipelines | Machine Learning, MLOps: Continuous delivery and automation pipelines in machine learning

Weighted class is a technique to rebalance a dataset by assigning different weights to each class, according to their frequency in the dataset. The weights are inversely proportional to the class frequency, meaning that rare classes have higher weights and common classes have lower weights. This helps to reduce the bias towards the majority class and improve the model performance on the minority class. References: 4. Data Validation - Building Machine Learning Pipelines, A guide to React design patterns - LogRocket Blog

Reinforcement learning is a type of machine learning that involves learning from trial and error based on rewards and penalties. Reinforcement learning can be used to develop models for dynamic pathing, which is the problem of finding an optimal path from one point to another in an uncertain and changing environment. Reinforcement learning can enable the model to adapt to new situations and learn from its own actions and feedback. For example, a self-driving car company can use reinforcement learning to train its model to navigate complex traffic scenarios and avoid collisions .

To calculate the minimum grade needed to achieve an 80% average, we can use the following formula:

minimum grade = (target average * number of tests - sum of grades) / (number of tests - 1)

Plugging in the given values, we get:

minimum grade = (80 * 7 - (76 + 81 + 78 + 87 + 75 + 72)) / (7 - 6)

minimum grade = (560 - 469) / 1

minimum grade = 91

Therefore, the student needs to score at least 91 on the last test to get an 80% average.

Lasso regression and ridge regression are both types of linear regression models that can handle high-dimensional and categorical data. They use regularization techniques to reduce the complexity of the model and avoid overfitting. Lasso regression uses L1 regularization, which adds a penalty term proportional to the absolute value of the coefficients to the loss function. This can shrink some coefficients to zero and perform feature selection. Ridge regression uses L2 regularization, which adds a penalty term proportional to the square of the coefficients to the loss function. This can shrink all coefficients towards zero and reduce multicollinearity. References: [Lasso (statistics) - Wikipedia], [Ridge regression - Wikipedia]

A false negative is an error where a positive case (belonging to the target class) is incorrectly predicted as negative (not belonging to the target class). A false negative rate is the ratio of false negatives to all actual positive cases. A low false negative rate means that most of the positive cases are correctly identified by the classifier.

For a diagnostic tool that can diagnose a particular disease so that individuals with the disease can receive treatment, it is of primary importance that it has a low false negative rate. This is because false negatives can have serious consequences for patients who have the disease but do not receive treatment, such as increased risk of mortality or complications. A low false negative rate can ensure that most patients who have the disease are diagnosed correctly and receive timely treatment.