Top 10 Machine Learning interview questions and answers

Training Set

• The data in the training set are the examples provided to the model to train that particular model.
• Usually, around 70-80% of the data is used for training purposes. The number is completely up to the user. However, having a higher amount of training data than testing data is recommended.
• To train the model, the training set is the labeled data that is used.

Test Set

• The data in the test are used to test the model accuracy of the already trained model.
• The Test Set contains around 20%-30% of the total data. This data is then further used to test the accuracy of the trained model.
• For testing purposes, labeled data is not used at all, however, the results are further verified with the labels.

Bias

When a model makes predictions, a disparity between the model's prediction values and actual values arises, and this difference is known as bias. Bias is the incapacity of machine learning algorithms like Linear Regression to grasp the real relationship between data points.

Variance

If alternative training data were utilized, the variance would describe the degree of variation in the prediction. In layman's terms, variance describes how far a random variable deviates from its predicted value.

In order to handle some missing or corrupted data, the easiest way is to just replace the corresponding rows and columns, which contain the incorrect data, with some different values. The two most useful functions in Panda for this purpose are isnull() and fillna().

• isnull(): is used to find missing values in a dataset
• fillna(): is used to fill missing values with 0’s

A decision tree uses a tree structure to generate any regression or classification models. While the decision tree is developed, the datasets are split up into ever-smaller subsets in a tree-like manner with branches and nodes. Decision trees can handle both category and numerical data.

Kernel SVM stands for Kernel Support Vector Machine. In SVM, a kernel is a function that aids in problem-solving. They provide shortcuts to help you avoid doing complicated math. The amazing thing about kernel is that it allows us to go to higher dimensions and execute smooth computations. Additionally, kernels allow us to go up to an unlimited number of dimensions.

One of the best ways to evaluate a logistic regression model is to use a confusion matrix, which is a very specific table that is used to measure the overall performance of any algorithm.

Using a confusion matrix, you can easily calculate the Accuracy Score, Precision, Recall, and F1 score. These can be extremely good indicators for your logistic regression model.

If the recall of your model is low, then it means that your model has too many False Negatives. Similarly, if the precision of your model is low, it signifies that your model has too many False Positives. In order to select a model with a balanced precision and recall score, the F1 Score must be used.

To start a Linear Regression model, there are some fundamental assumptions that you need to make:

• The model should have a multivariate normal distribution
• There should be no auto-correlation
• Homoscedasiticity, i.e, the dependent variable’s variance should be similar to all of the data
• There should be a linear relationship
• There should be no or almost no multicollinearity present

If there is a correlation between the independent variables in a regression model, it is known as multicollinearity. Multicollinearity is an area of concern as independent variables should always be independent. When you fit the model and analyze the findings, a high degree of correlation between variables might present complications.

There are various ways to check and handle the presence of multicollinearity in your regression model. One of them is to calculate the Variance Inflation Factor (VIF). If your model has a VIF of less than 4, there is no need to investigate the presence of multicollinearity. However, if your VIF is more than 4, an investigation is very much required, and if VIF is more than 10, there are serious concerns regarding multicollinearity, and you would need to correct your regression model.

XGBoost is an ensemble approach that employs a large number of trees. This implies that when it repeats itself, it becomes better.

If our data isn't linearly separable, SVM, being a linear separator, will need to use a Kernel to bring it to a point where it can be split. Due to there not being an ideal Kernel for every dataset, this can be limiting.

An encoder-decoder model is used to create an output sequence based on a given input sequence. The final state of the encoder is used as the initial state of the decoder, and this makes the encoder-decoder model extremely powerful. This also allows the decoder to access the information that is taken from the input sequence by the encoder.