In Q&A

1. What Are the Different Types of Machine Learning?
There are three types of machine learning:
Supervised Learning
In supervised machine learning, a model makes predictions or decisions based on past or labeled data. Labeled data refers to sets of data that are given tags or labels, and thus made more meaningful.
Unsupervised Learning
In unsupervised learning, we don’t have labeled data. A model can identify patterns, anomalies, and relationships in the input data.
Reinforcement Learning
Using reinforcement learning, the model can learn based on the rewards it received for its previous action.
Consider an environment where an agent is working. The agent is given a target to achieve. Every time the agent takes some action toward the target, it is given positive feedback. And, if the action taken is going away from the goal, the agent is given negative feedback.

2. What is Overfitting, and How Can You Avoid It?
The Overfitting is a situation that occurs when a model learns the training set too well, taking up random fluctuations in the training data as concepts. These impact the model’s ability to generalize and don’t apply to new data.
When a model is given the training data, it shows 100 percent accuracy—technically a slight loss. But, when we use the test data, there may be an error and low efficiency. This condition is known as overfitting.
There are multiple ways of avoiding overfitting, such as:
Regularization. It involves a cost term for the features involved with the objective function
Making a simple model. With lesser variables and parameters, the variance can be reduced
Cross-validation methods like k-folds can also be used
If some model parameters are likely to cause overfitting, techniques for regularization like LASSO can be used that penalize these parameters

3. What is ‘training Set’ and ‘test Set’ in a Machine Learning Model? How Much Data Will You Allocate for Your Training, Validation, and Test Sets?
There is a three-step process followed to create a model:
Train the model
Test the model
Deploy the model
The training set is examples given to the model to analyze and learn
The test set is used to test the accuracy of the hypothesis generated by the model
70% of the total data is typically taken as the training dataset
Remaining 30% is taken as testing dataset
Consider a case where you have labeled data for 1,000 records. One way to train the model is to expose all 1,000 records during the training process. Then you take a small set of the same data to test the model, which would give good results in this case.
But, this is not an accurate way of testing. So, we set aside a portion of that data called the ‘test set’ before starting the training process. The remaining data is called the ‘training set’ that we use for training the model. The training set passes through the model multiple times until the accuracy is high, and errors are minimized.
Now, we pass the test data to check if the model can accurately predict the values and determine if training is effective. If you get errors, you either need to change your model or retrain it with more data.

Regarding the question of how to split the data into a training set and test set, there is no fixed rule, and the ratio can vary based on individual preferences.

4. How Do You Handle Missing or Corrupted Data in a Dataset?
One of the easiest ways to handle missing or corrupted data is to drop those rows or columns or replace them entirely with some other value.
There are two useful methods in Pandas:
IsNull() and dropna() will help to find the columns/rows with missing data and drop them
Fillna() will replace the wrong values with a placeholder value

5. How Can You Choose a Classifier Based on a Training Set Data Size?
When the training set is small, a model that has a right bias and low variance seems to work better because they are less likely to overfit.
For example, Naive Bayes works best when the training set is large. Models with low bias and high variance tend to perform better as they work fine with complex relationships.

6. Explain the Confusion Matrix with Respect to Machine Learning Algorithms.
A confusion matrix (or error matrix) is a specific table that is used to measure the performance of an algorithm. It is mostly used in supervised learning; in unsupervised learning, it’s called the matching matrix.
The confusion matrix has two parameters:
It also has identical sets of features in both of these dimensions.

7. What Is a False Positive and False Negative and How Are They Significant?
False positives are those cases that wrongly get classified as True but are False.
False negatives are those cases that wrongly get classified as False but are True.
In the term ‘False Positive,’ the word ‘Positive’ refers to the ‘Yes’ row of the predicted value in the confusion matrix. The complete term indicates that the system has predicted it as a positive, but the actual value is negative.

8. 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.

9. What is Deep Learning?
The Deep learning is a subset of machine learning that involves systems that think and learn like humans using artificial neural networks. The term ‘deep’ comes from the fact that you can have several layers of neural networks.

One of the primary differences between machine learning and deep learning is that feature engineering is done manually in machine learning. In the case of deep learning, the model consisting of neural networks will automatically determine which features to use (and which not to use).

10. How Will You Know Which Machine Learning Algorithm to Choose for Your Classification Problem?
While there is no fixed rule to choose an algorithm for a classification problem, you can follow these guidelines:
If accuracy is a concern, test different algorithms and cross-validate them
If the training dataset is small, use models that have low variance and high bias
If the training dataset is large, use models that have high variance and little bias

11. What Are the Applications of Supervised Machine Learning in Modern Businesses?
Applications of supervised machine learning include:
Email Spam Detection
Here we train the model using historical data that consists of emails categorized as spam or not spam. This labeled information is fed as input to the model.
Healthcare Diagnosis
By providing images regarding a disease, a model can be trained to detect if a person is suffering from the disease or not.
Sentiment Analysis
This refers to the process of using algorithms to mine documents and determine whether they’re positive, neutral, or negative in sentiment.
Fraud Detection
By training the model to identify suspicious patterns, we can detect instances of possible fraud.

12. What is Semi-supervised Machine Learning?
Supervised learning uses data that is completely labeled, whereas unsupervised learning uses no training data.
In the case of semi-supervised learning, the training data contains a small amount of labeled data and a large amount of unlabeled data.

13. What Are Unsupervised Machine Learning Techniques?
There are two techniques used in unsupervised learning: clustering and association.
Clustering problems involve data to be divided into subsets. These subsets, also called clusters, contain data that are similar to each other. Different clusters reveal different details about the objects, unlike classification or regression.
In an association problem, we identify patterns of associations between different variables or items.
For example, an e-commerce website can suggest other items for you to buy, based on the prior purchases that you have made, spending habits, items in your wishlist, other customers’ purchase habits, and so on.

14. What is the Difference Between Supervised and Unsupervised Machine Learning?
Supervised learning – This model learns from the labeled data and makes a future prediction as output
Unsupervised learning – This model uses unlabeled input data and allows the algorithm to act on that information without guidance.

15. What Is ‘naive’ in the Naive Bayes Classifier?
The classifier is called ‘naive’ because it makes assumptions that may or may not turn out to be correct.
The algorithm assumes that the presence of one feature of a class is not related to the presence of any other feature (absolute independence of features), given the class variable.
For instance, a fruit may be considered to be a cherry if it is red in color and round in shape, regardless of other features. This assumption may or may not be right (as an apple also matches the description).

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