If you’re looking to break into the field of machine learning, it’s essential to understand the importance of model evaluation. Evaluating machine learning models is a crucial step in the data science pipeline, and is used to assess the accuracy and reliability of predictive models.
Importance of model evaluation
Model evaluation is important for several reasons. Firstly, it helps machine learning specialists to identify any errors or inconsistencies in their models, which can be corrected to improve overall performance. Secondly, model evaluation helps to determine whether the model is suitable for its intended use case, and whether any further optimization or parameter tuning is necessary. As a job-seeker in machine learning, having a solid understanding of model evaluation is crucial to demonstrate your skills and knowledge to potential employers.
Preparing for model evaluation in technical interviews
Preparing for model evaluation in the technical interview can be challenging, but it’s essential to increase your chances of success. Here are some tips to help you prepare:
- Review the key concepts and techniques in model evaluation, such as evaluation metrics, overfitting, and cross-validation.
- Practice applying these concepts in real-world scenarios by working on example problems and datasets.
- Familiarize yourself with common machine learning algorithms and their strengths and weaknesses.
- Review the job description and requirements carefully to understand what the employer is looking for.
- Be prepared to explain your thinking and reasoning behind your answers in detail, as interviewers may ask follow-up questions.
- Practice communicating your findings and recommendations in a clear and concise manner.
Use the questions to prepare for a technical interview
Here are 7 typical questions about the model evaluation you can expect to encounter in a technical interview for machine learning. These questions were designed together with experts from skillfill.ai. If you need more questions, just visit skillfill.ai.
Question 1. Imagine you train two binary classification models. To compare their performance, you calculate the True Positive Rate and the False Positive Rate as a function of the cutoff threshold, see below. Which model performs better?
| Option A) | Random Forest | Option B) | Logistic Regression |
| Option C) | Both perform equally | Option D) | Can’t tell based on the plotted data. |
Correct answer: (A)
Explanation: With this question you are evaluating logistic regression and model evaluation. The optimum point in a TPR-FPR plot usually lies at the point where the TPR is maximized while keeping the FPR at a minimal value. This point represents the optimal balance between the two metrics and is often referred to as the “receiver operating characteristic” (ROC) curve. The optimum point on the ROC curve can be identified as the point closest to the top left corner, where the TPR is 1 and the FPR is 0. The closer the ROC curve is to the top left corner, the better the model is at distinguishing between positive and negative samples.
Question 2. The three scatter plots show a binary classification problem in a 2-dimensional feature space (x1, x2). The lines mark the decision boundaries resulting from three different non-linear logistic regression models. Which of the three models shows the highest variance?
| Option A) | A | Option B) | B |
| Option C) | C | Option D) | Can’t say |
Correct answer: (C)
Explanation: This questions shows if they applicant understands about data classification and model evaluation.
The correct answer is C since high variance means your model is overfitting, therefore fitting the training data (almost) perfectly but not generalizing well enough on new data. Out of the three examples, C fits the data basically perfectly, hence showing the highest variance. Example B underfits the data therefore showing low variance and example A sits in between.
Question 3. Select the correct statement(s) about K-fold Cross-Validation. [multiple solutions possible]
| Option A) | By increasing the number K of groups to split up the data set, the run-time of the process is increased. | Option B) | Lower K values result in lower confidence on the overall cross-validation result. |
| Option C) | If the number of groups K is chosen to be the same as observations N in the data set, you perform Leave-One-Out Cross-Validation. | Option D) | Can’t say |
Correct answer: (A), (B), and (C)
Explanation: Now you focus on important skills for a mid-level Machine Learning engineer such as model improvement, and model training. Here’s an explanation of why a, b and c are correct:
-
-
- Increasing the number of folds (K) in k-fold cross validation will increase the run-time of the process, as the data will be split into more parts and each part will be used as a validation set once.
- Correct because with a lower number of folds, each fold represents a larger proportion of the data, leading to a higher variance in the estimated model performance.
- True since in LOOCV, each observation is used exactly once as the validation set, and the remaining data is used for training.
-
Question 4. The plot below shows two features A and B describing a binary classification problem (dots are class 1, crosses are class 0). Using a decision tree algorithm, you can split the data based on feature A (horizontal axis) or feature B (vertical axis). Here, a vertical split is applied where smaller values for A result in class 1, and larger values for A result in class 0. What is the accuracy of this decision’s classification?
| Option A) | 100% | Option B) | 16,70% |
| Option C) | 33,30% | Option D) | 83,30% |
Correct answer: (D)
Explanation: The skills being evaluated are data classification, and decision trees.
Since the straight line classifies three dots (1) to the left of the vertical line and two crosses (0) to the right of the line correctly, only one cross (0) is misclassified. Hence, the accuracy is (6-1)/6 = 5/6 = 83.3%.
Question 5. A classification problem to predict good vs. bad wines is approached by training three different algorithms: random forest model, a support vector machine and a multilayer perceptron. The resulting confusion matrices on the test set are given below. Which model is the most accurate?
| Option A) | Support vector machine | Option B) | Random forest |
| Option C) | Multilayer perceptron | Option D) | Can’t say. |
Correct answer: (B)
Explanation: Now you are evaluating important skills for a Machine Learning engineer like data classification, model evaluation, and scikit-learn.
The accuracy of a model can be calculated from a confusion matrix using the following equation: Accuracy = (TP + TN)/(TP + TN + FP + FN) Where TP = True Positives, TN = True Negatives, FP = False Positives, and FN = False Negatives. Doing the math on these: Random Forest = 289/320, Support Vector = 280/320, Multilayer Perceptron = 286/320. Hence, the random forest is the most accurate.
Question 6. Your decision tree algorithm splits different categorical features applied on a problem to predict whether a car is fast or slow according to the plot below. Which feature shows the highest information gain?
| Option A) | Cylinder | Option B) | Car colour |
| Option C) | Horsepower | Option D) | Manufacturer continent |
Correct answer: (A)
Explanation: Here you are assessing the knowledge in model evaluation, and decision trees.
In a tree-based algorithm such as decision trees, the feature with the highest information gain is typically selected as the root node or the top splitting feature for each node in the tree. Information gain measures the reduction in entropy or the increase in the purity of the target variable after splitting the data on a particular feature. By selecting the feature with the highest information gain, the algorithm aims to maximize the reduction in entropy, leading to the best possible splits and more accurate predictions. In this example, only cylinder can separate at least one branch into purely one classification (slow for V6) whereas all branches of all other features have mixed classifications (both slow and fast).
Question 7. When training decision trees, the information gain can be calculated. This is a measure used to decide for the amount of information gained from the features towards the dependent variable. Another measure is the ratio between the information gain and the intrinsic value of a feature. For categorical features, when is it better to use information gain compared to the gain ratio?
| Option A) | When the categorical feature has a high cardinality. | Option B) | When the categorical feature has a low cardinality. |
| Option C) | Cardinality of a categorical feature should not influence the choice of the right measure. | Option D) | Can’t say |
Correct answer: (B)
Explanation: With this question you check the knowledge regarding decision trees.
If the cardinality of a feature is low (e.g., a binary feature), then the information gain is likely to work well as a feature selection criterion, as the intrinsic information content of the feature is relatively low.
Prepare for the tech interview with AboutSkills and land your dream job in Data!
