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### Data Science Simplified Part 11: Logistic Regression

In the last blog post of this series, we discussed classifiers. The categories of classifiers and how they are evaluated were discussed. We have also discussed regression models in depth. In this post, we dwell a little deeper in how regression models can be used for classification tasks.

### Tour of Evaluation Metrics for Imbalanced Classification

A classifier is only as good as the metric used to evaluate it. If you choose the wrong metric to evaluate your models, you are likely to choose a poor model, or in the worst case, be misled about the expected performance of your model. Choosing an appropriate metric is challenging generally in applied machine learning, but is particularly difficult for imbalanced classification problems. Firstly, because most of the standard metrics that are widely used assume a balanced class distribution, and because typically not all classes, and therefore, not all prediction errors, are equal for imbalanced classification. In this tutorial, you will discover metrics that you can use for imbalanced classification. Tour of Evaluation Metrics for Imbalanced Classification Photo by Travis Wise, some rights reserved.

### An in-depth guide to supervised machine learning classification

In supervised learning, algorithms learn from labeled data. After understanding the data, the algorithm determines which label should be given to new data by associating patterns to the unlabeled new data. Supervised learning can be divided into two categories: classification and regression. Some examples of classification include spam detection, churn prediction, sentiment analysis, dog breed detection and so on. Some examples of regression include house price prediction, stock price prediction, height-weight prediction and so on.

### The 5 Classification Evaluation metrics every Data Scientist must know

What do we want to optimize for? Most of the businesses fail to answer this simple question. Every business problem is a little different, and it should be optimized differently. We all have created classification models. A lot of time we try to increase evaluate our models on accuracy.

### The 5 Classification Evaluation Metrics Every Data Scientist Must Know - KDnuggets

What do we want to optimize for? Most of the businesses fail to answer this simple question. Every business problem is a little different, and it should be optimized differently. We all have created classification models. A lot of time we try to increase evaluate our models on accuracy.