Linear regression is a modeling approach that attempts to establish a relationship between a dependent variable and one or more independent variables by fitting a linear equation. There are five assumptions of linear regression. This is what is going to be covered in this blog post.

Linear Relationship

All features should…

Decision trees are algorithms that partition the sample space into sets containing similar points. The top feature in the decision tree is called root node, the nodes in between are called internal nodes, and the final nodes are called leaf nodes. Essentially, each node partitions the space into two subspaces…

Sentiment Analysis is a sub-field of Natural Language processing that focuses on interpreting and classifying emotions using text data. This week, I worked on a small project with the goal of classifying products’ reviews into positive, negative, and neutral. For this purpose, I used three python libraries: TextBlob, AFINN…

1. What is the difference between Type I and Type II errors?

Type I error is a false positive; when we reject “a true null hypothesis”.

Type II error is a false negative; when we fail to reject “a false null hypothesis”.

2. Are expected value and mean value different?

The concept behind the two is the same, however, they are used in different contexts. The expected value is used…

1. What is a decision tree model?

The decision tree model is a machine learning model in which the algorithm is considered to be a tree. Essentially, it partitions the sample space into sets with points homogeneous or close to one another. Compared to other algorithms, these models are simple to understand and explain. …

1. What are tensors?

Tensor is a generalization of an n-dimensional array.

  • A scalar is a 0-dimensional tensor.
  • A vector is a 1-dimensional tensor.
  • A matrix is a 2-dimensional tensor.

If your array has 3 or more dimensions, they are just called tensors.

2. Why do you use tensors in deep learning?

Statistically, tensors allow the representation of higher dimensional relationships in…

1. What is Selection Bias?

Selection bias is a bias that results from failing to properly select a random population sample. This happens when there are flaws in the selection process such as:

  • Self-selection: when the participants can choose whether or not to participate in the study.
  • Selection from a specific area
  • Exclusion of some…

Edna Figueira Fernandes

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