Is ARIMA model a good fit for every time series problem?

No, ARIMA model is not suitable for every type of time series problem. There are situations where ARMA model and others also come in handy.

ARIMA is best when different standard temporal structures require to be captured for time series data.

What is inductive machine learning?

The inductive machine learning involves the process of learning by examples, where a system, from a set of observed instances tries to induce a general rule.

What cross validation technique would you use on time series data set? Is it k-fold or LOOCV?

Neither.

In time series problem, k fold can be troublesome because there might be some pattern in year 4 or 5 which is not in year 3. Resampling the data set will separate these trends, and we might end up validation on past years, which is incorrect. Instead, we can use forward chaining strategy with 5 fold as shown below:

fold 1 : training [1], test [2]
fold 2 : training [1 2], test [3]
fold 3 : training [1 2 3], test [4]
fold 4 : training [1 2 3 4], test [5]
fold 5 : training [1 2 3 4 5], test [6]
where 1,2,3,4,5,6 represents “year”.

What Are the Three Stages of Building a Model in Machine Learning?

The three stages of building a machine learning model are:

Model Building
Choose a suitable algorithm for the model and train it according to the requirement
Model Testing
Check the accuracy of the model through the test data
Applying the Model
Make the required changes after testing and use the final model for real-time projects
Here, it’s important to remember that once in a while, the model needs to be checked to make sure it’s working correctly. It should be modified to make sure that it is up-to-date.

What do you understand by L1 and L2 regularization?

L2 regularization: It tries to spread error among all the terms. L2 corresponds to a Gaussian prior.

L1 regularization: It is more binary/sparse, with many variables either being assigned a 1 or 0 in weighting. L1 corresponds to setting a Laplacean prior on the terms.

How can you avoid overfitting?

Overfitting happens when a machine has an inadequate dataset and it tries to learn from it. So, overfitting is inversely proportional to the amount of data.

For small databases, we can bypass overfitting by the cross-validation method. In this approach, we will divide the dataset into two sections. These two sections will comprise testing and training sets. To train the model, we will use the training dataset and, for testing the model for new inputs, we will use the testing dataset.

This is how we can avoid overfitting.

What cross-validation technique would you use on a time series dataset?

Instead of using standard k-folds cross-validation, you have to pay attention to the fact that a time series is not randomly distributed data—it is inherently ordered by chronological order. If a pattern emerges in later time periods, for example, your model may still pick up on it even if that effect doesn’t hold in earlier years!

You’ll want to do something like forward chaining where you’ll be able to model on past data then look at forward-facing data.

Fold 1 : training [1], test [2]
Fold 2 : training [1 2], test [3]
Fold 3 : training [1 2 3], test [4]
Fold 4 : training [1 2 3 4], test [5]
Fold 5 : training [1 2 3 4 5], test [6]

How do you handle missing or corrupted data in a dataset?

You could find missing/corrupted data in a dataset and either drop those rows or columns, or decide to replace them with another value.

In Pandas, there are two very useful methods: isnull() and dropna() that will help you find columns of data with missing or corrupted data and drop those values. If you want to fill the invalid values with a placeholder value (for example, 0), you could use the fillna() method.

Mention some of the EDA Techniques?

Exploratory Data Analysis (EDA) helps analysts to understand the data better and forms the foundation of better models.

Visualization

Univariate visualization
Bivariate visualization
Multivariate visualization
Missing Value Treatment – Replace missing values with Either Mean/Median

Outlier Detection – Use Boxplot to identify the distribution of Outliers, then Apply IQR to set the boundary for IQR

Transformation – Based on the distribution, apply a transformation on the features

Scaling the Dataset – Apply MinMax, Standard Scaler or Z Score Scaling mechanism to scale the data.

Feature Engineering – Need of the domain, and SME knowledge helps Analyst find derivative fields which can fetch more information about the nature of the data

Dimensionality reduction — Helps in reducing the volume of data without losing much information

What are ensemble models? Explain how ensemble techniques yield better learning as compared to traditional classification ML algorithms?

Ensemble is a group of models that are used together for prediction both in classification and regression class. Ensemble learning helps improve ML results because it combines several models. By doing so, it allows a better predictive performance compared to a single model.
They are superior to individual models as they reduce variance, average out biases, and have lesser chances of overfitting.