# Key Prescreening Questions to Ask Machine Learning Engineer for Efficient Candidate Selection

As the field of technology continues to boom, machine learning has become a crucial element. Understanding this critical area requires the ability to answer some of the most significant questions on the topic. In this article, we delve into 20 of those questions and their answers in detail. This handy guide aims to provide a brief yet comprehensive understanding of machine learning.

## What are the differences between supervised and unsupervised learning?

In supervised learning, the model is taught using labeled data. On the other hand, unsupervised learning involves training models using unlabeled data. It implies that in supervised learning the output is known while in unsupervised learning, the output is unknown, and the model is left to find patterns on its own.

## Can you explain how a decision tree works?

A decision tree is a graphical representation that makes decisions based on conditions. It works by dividing the data into subsets based on variables. These divisions are made until a specific condition under evaluation is met.

## What is the bias-variance trade-off in machine learning?

The bias-variance trade-off is a balance that needs to be found between being too flexible and too rigid when fitting a model to the data. Any deviation from the balance can cause either underfitting or overfitting, affecting the model’s credibility.

## Can you define precision and recall in the context of machine learning?

Precision is the accuracy of true positive predictions in relation to the total predicted positives. Recall, on the other hand, is the proportion of true positives that have been correctly identified.

## Describe what is meant by 'overfitting' in relation to machine learning models.

Overfitting occurs when a model is so closely fit to the training data that it performs poorly on new, unseen data. It means the model has learned the noise and outliers structure so well that it negatively impacts the model’s ability to generalize.

## Can you explain what regularization is and why it is useful?

Regularization is a technique used to prevent overfitting in machine learning models. It works by adding a penalty term to the objective function which reduces the complexity of the model, making it more generalized and thus more robust against new data.

## How would you validate a model you created to generate a predictive analysis?

Model validation primarily includes two steps - training and testing. Training the model involves using a portion of the dataset, usually around 70%. Testing uses the remaining 30% to verify the accuracy of the model's predictions.

## What are the differences between L1 and L2 regularization?

L1 regularization has the capacity to exclude uninformative variables from a model, leading to a model with fewer parameters. On the other hand, L2 regularization does not exclude variables but rather reduces their impact on the model.

## Can you explain what Principal Component Analysis (PCA) is?

PCA is a technique used to deal with high-dimensional data. The process involves transforming the data into a set of successive orthogonal components that capture the maximum amount of variance in the data.

## What is the difference between a parametric learning algorithm and a non-parametric learning algorithm?

A parametric learning algorithm assumes the functional form, or shape, of the data distribution. In contrast, a non-parametric learning algorithm doesn’t assume the functional form of data and tends to have more flexibility but requires more data and computational power.

## What is the purpose of a cost function in machine learning?

A cost function essentially measures how off a prediction model is from the actual result. It aids in the evaluation and comparison of different models during the machine learning algorithm process.

## What experience do you have with distributed computing systems like Hadoop or Spark?

Candidates' responses to this question will vary depending on their individual experiences. This question primarily seeks to understand the candidate's familiarity and comfort level with distributed computing systems.

## Do you have experience working with large datasets?

Once again, answers to this question will depend on personalized experiences. It is crucial to know if a candidate can effectively handle and manipulate large amounts of data considering the real-time proliferation of big data.

## How would you deal with an imbalanced dataset?

Imbalanced datasets can be handled by implementing resampling techniques. One can oversample the minority class, undersample the majority class, or generate synthetic samples.

## How would you evaluate a logistic regression model?

Evaluating a logistic regression model can be done using a confusion matrix determining its precision, recall, F1 score, and receiver operating characteristics curve (ROC).

## Have you implemented a machine learning algorithm from scratch? If So, which one?

Depending on the individual's experience and expertise, the response to this question will vary. It is designed to gauge their depth of knowledge and practical experience in the field.

## Can you explain the concept of 'Embedding' in Machine Learning?

Embedding in machine learning involves converting categorical variables into continuous vectors. This is done to help the machine learning algorithms understand and make better predictions.

## How familiar are you with neural network structures?

Again, the answer to this question will rely heavily on one's personal experience and exposure to neural network structures.

## What is the role of the activation function in a Neural network?

The activation function introduces non-linearity into the network which allows the model to learn from complex patterns.

## How would you handle missing or corrupted data in a dataset?

Missing or corrupted data can be handled by either deleting rows with missing data, filling in missing values, or predicting missing values using techniques like k-nearest neighbors or deep learning.

##### Prescreening questions for Machine Learning Engineer

- What are the differences between supervised and unsupervised learning?
- Can you explain how a decision tree works?
- What is the bias-variance trade-off in machine learning?
- Can you define precision and recall in the context of machine learning?
- Describe what is meant by 'overfitting' in relation to machine learning models.
- Can you explain what regularization is and why it is useful?
- How would you validate a model you created to generate a predictive analysis?
- What are the differences between L1 and L2 regularization?
- Can you explain what Principal Component Analysis (PCA) is?
- What is the difference between a parametric learning algorithm and a nonparametric learning algorithm?
- What is the purpose of a cost function in machine learning?
- What experience do you have with distributed computing systems like Hadoop or Spark?
- Do you have experience working with large datasets? If so, could share an experience where you had to clean or manipulate large data?
- How would you deal with an imbalanced dataset?
- How would you evaluate a logistic regression model?
- Have you implemented a machine learning algorithm from scratch? If So, which one?
- Can you explain the concept of 'Embedding' in Machine Learning?
- How familiar are you with neural network structures?
- What is the role of the activation function in a Neural network?
- How would you handle missing or corrupted data in a dataset?

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