What You Will Learn
- 1 Machine learning Interview Questions
- 1.1 1.What is the difference between supervised and unsupervised machine learning?
- 1.2 2.Define Precision and Recall.
- 1.3 3.Why is ‘’naïve’’ Bayes naïve?
- 1.4 4.What is False Positive and False Negative?
- 1.5 5.What are the three stages of building a Model in Machine Learning?
- 1.6 6.How would you handle an imbalanced dataset?
- 1.7 7.When should you use classification over regression?
- 1.8 8.How do you know which machine is learning the algorithm for choosing for your classification problem?
- 1.9 9.What is a Random Forest?
- 1.10 10.What is a Decision Tree Classification?
- 1.11 11.What is Kernel SVM?
- 1.12 12.How do you handle missing or corrupted data in a dataset?
- 1.13 13.Describe a hash table.
- 1.14 14.What are some Methods for Reducing Dimensionality?
- 1.15 15.What is the Recommendation System?
- 1.16 16.How Do You Design an Email Spam Filter?
- 1.17 17.How is KNN different from K-means Clustering?
- 1.18 18.Explain how a ROC curve works.
- 1.19 19.what is the difference between L1 and L2 regularization?
- 1.20 20.What’s your favorite algorithm, and can you explain it to me in less than a minute?
Machine learning Interview Questions
Here are the basic 20 Machine learning Interview questions that can help you during the interview. Read the full article. Hope it will help you.
1.What is the difference between supervised and unsupervised machine learning?
Supervised learning requires labeled data training. For example, in order to classify, you will first need to label the data that you will use to train the model to classify the data in your labeled groups. In contrast, unsupervised learning does not require explicit data.
2.Define Precision and Recall.
The recall is also known as the actual positive rate. The positive amount of your model’s claims compared to the actual amount of positivity in the data. Precision is also known as a positive value, and it measures the positive amount of the model’s claims and compared to the number it actually claims.
3.Why is ‘’naïve’’ Bayes naïve?
Despite its practical use, especially in text mining, bidding is considered ‘’naïve’’ because it makes the assumption that it is practically impossible to see in real-life statistics. Considered as a pure product of individual possibilities. This shows that absolute freedom of character is a condition that may never be found in real life.
4.What is False Positive and False Negative?
False positives are those cases that wrongly get classified as True but are False.
False-negative are cases that are incorrectly classified as false but they are true.
5.What are the three stages of building a Model in Machine Learning?
The three stages model are:
- Model Building
- Model Testing
- Applying the Model
6.How would you handle an imbalanced dataset?
An imbalanced dataset occurs when you have, for example, a rating test and 90% of the data in a class this causes the problem. 90% accuracy can be scanned if you do not have the power to make predictions on other types of data.
7.When should you use classification over regression?
Classification gives discrete values and is ready datasets, the regression results in permanent severe forms that allow you to better distinguish the individual points. If you want your results to show the association of data points in a specific category in your dataset, you will use a ranking on regression.
8.How do you know which machine is learning the algorithm for choosing for your classification problem?
Although there is no set rule for choosing the algorithm for the classification problem, you can follow these instructions:
1: If accuracy is a concern, test different algorithms and correct them
2: If the training dataset is small, use models that have less variability and more bias.
3: If the training dataset is large, use models with more variability and less bias.
9.What is a Random Forest?
A ‘random forest’ is a supervised machine learning algorithm commonly used for classification difficulties. It works by building several deciduous trees during the training phase. The random forest chooses the decision of the majority of trees as the final decision.
10.What is a Decision Tree Classification?
The decision tree forms a model of classification (or regression) as a tree structure, the datasets always break down into smaller subsets, like a tree with branches and nodes. Both categorical and numerical data decision trees can handle
11.What is Kernel SVM?
Kernel SVM is a shortened version of the kernel machine supporting. Kernel methods are a class of pattern analysis algorithms, and the most common one is Kernel SVM.
12.How do you handle missing or corrupted data in a dataset?
You find missing/corrupted data in the dataset and can either drop rows or columns or decide to replace it with another value.
13.Describe a hash table.
Answer: A hash table is a data structure that creates an associative array. Some keys are mapped to a key using the hash function. They are often used for tasks such as database indexing.
14.What are some Methods for Reducing Dimensionality?
You can reduce the dimensions by combining features with feature engineering, removing the collinear features, or using algorithm reduction.
15.What is the Recommendation System?
Anyone who has used or purchased Spotify at Amazon will recognize a recommendation system: it is an information filtering system that predicts what the user will hear or see based on the selection patterns provided by the user Wants.
16.How Do You Design an Email Spam Filter?
Building a spam filter involves the following process:
- The email spam filter will be fed with thousands of emails
- Each of these emails is already labeled: ‘spam’ or ‘not spam.’
- The supervised machine learning algorithm will then determine what type of spam-based emails are being marked as spam.
- The next time an email is about to be removed from your inbox, the spam filter will use data analysis and algorithms such as Decision Trees and SVM to determine if the email is spam. How likely is it to happen?
- If more likely, it will label it as spam, and the email will not reach your inbox
- Based on the accuracy of each model, we will use algorithms with high accuracy after testing all the models.
17.How is KNN different from K-means Clustering?
K-nearest Neighbors is a classification algorithm, while K-means clustering is an unsupervised clustering algorithm. Although this mechanism can be found first, it actually means that in order to work with the nearest neighbors, you need a labeled data in which you want to classify an unlabeled point.
18.Explain how a ROC curve works.
The ROC is a curvilinear pattern that contrasts between true positive rates and false-positive rates at different levels.
19.what is the difference between L1 and L2 regularization?
L2 has to regularly spread error in all terms, while L1 is more binary/sparse, many variables are assigned either 1 or 0 in weight
20.What’s your favorite algorithm, and can you explain it to me in less than a minute?
This type of question tests your understanding of how to communicate with the ability to summarize complex and technical nuances in a concise and fast and efficient manner.
After Reading Machine learning interview questions You may also like to read: Transfer Learning – What are the types of Transfer Learning?