Data Science / ML #2

From the following statements, which ones are correct representations of Overfitting and Underfitting ?

• Overfitting happens when your model is too complex for your dataset. For example, a very deep neural network trying to learn a few dozen samples with a couple of features.

• Underfitting happens when your model is too simple for your dataset. For example, a linear regression model trying to learn a large dataset with hundreds of features.

• Overfitting happens when your model is too simple for your dataset. For example, a linear regression model trying to learn a large dataset with hundreds of features.

• Underfitting happens when your model is too complex for your dataset. For example, a very deep neural network trying to learn a few dozen samples with a couple of features.

A business want to categorize user behavior as fraudulent or normal. A Machine Learning Specialist want to develop a binary classifier based on two features: account age and transaction month. The graphic shown illustrates the class distribution of these characteristics.

Which model would have the HIGHEST degree of accuracy based on this information?

• Logistic regression

• Single perceptron with tanh activation function

• Long short-term memory (LSTM) model with scaled exponential linear unit (SELU)

• Support vector machine (SVM) with non-linear kernel

Within a nation, an organization gathers census data to ascertain healthcare and social program requirements by province and city. Each person responds to around 500 questions on the census form.

Which algorithmic combination would deliver the necessary insights?

• The factorization machines (FM) algorithm

• The Latent Dirichlet Allocation (LDA) algorithm

• The principal component analysis (PCA) algorithm

• The k-means algorithm

• The Random Cut Forest (RCF) algorithm

Disadvantages of Naïve Bayes Classifier

• Naive Bayes doesn't perform well in Multi-class predictions

• Naive Bayes is only useful for text classification

• Naive Bayes assumes that all features are independent or unrelated, so it cannot learn the relationship between

• Naïve Bayes algorithm is based on EM algorithm, so it is slow to train

A data scientist is constructing a machine learning model to determine the legitimacy of financial transactions. The labeled data provided for training consists of 100,000 observations that are not fraudulent and 1,000 observations that are fraudulent. When the trained model is applied to a previously unknown validation dataset, the Data Scientist obtains the following confusion matrix. Although the model is 99.1 percent accurate, the Data Scientist has been requested to minimize false negatives.

Which combination of procedures should the Data Scientist perform in order to minimize the model's false positive predictions?

• Decrease the XGBoost max_depth parameter because the model is currently overfitting the data.

• Increase the XGBoost scale_pos_weight parameter to adjust the balance of positive and negative weights.

• Increase the XGBoost max_depth parameter because the model is currently underfitting the data.

• Change the XGBoost eval_metric parameter to optimize based on AUC instead of error.

• Change the XGBoost eval_metric parameter to optimize based on rmse instead of error.