Evaluation metric for Supervised Learning

Evaluation metric for Supervised Learning:

Evaluation metrics explain the performance of a model. An important aspect of evaluation metrics is their capability to discriminate among model results.

In machine learning, we regularly deal with mainly two types of tasks that are classification and regression. Classification is a task where the predictive models are trained in a way that they are capable of classifying data into different classes for example if we have to build a model that can classify whether a loan applicant will default or not. …

Feature importance graph is not generating through below code, it is applicable only for LGBM.

Can you check and tell me any updates in code?

# plot feature importance

plot_importance(xgb_model)

plt.show()

What are outliers and how to deal with them?

In this post we will try to understand all about outliers by answering the following questions, and at the end of the paper, will use Python to create some examples.

  1. What Outlier is?
  2. How the Outlier are introduced in the datasets?
  3. How to detect Outliers?
  4. Why is it important to identify the outliers?
  5. What are the types of Outliers?
  6. What are the methods to prevent Outliers?

1. What Outlier is?

Outliers are those data points which differs significantly from other observations present in given dataset. …

AUC-ROC CURVE | CONFUSION MATRIX | SENSITIVITY | SPECIFICITY|

The Receiver Operator Characteristic (ROC) curve is an evaluation metric for binary classification problems. It is a probability curve that plots the TPR against FPR at various threshold values and essentially separates the ‘signal’ from the ‘noise’. The Area Under the Curve (AUC) is the measure of the ability of a classifier to distinguish between classes and is used as a summary of the ROC curve.

AUC-ROC curve is a performance measurement for the classification problems at various threshold settings. ROC is a probability curve, and AUC represents the degree or…

Everything you Should Know about Confusion Matrix for Machine Learning

A Confusion matrix is an N x N matrix used for evaluating the performance of a classification model, where N is the number of target classes. The matrix compares the actual target values with those predicted by the machine learning model.

EDA | Data Analysis | Python | Pandas |

Exploratory Data Analysis (EDA)

1. What Is Exploratory Data Analysis In Python?

Exploratory Data Analysis (EDA) in Python is the first step in your data analysis process developed by “John Tukey” in the 1970s. In statistics, exploratory data analysis is an approach to analyzing data sets to summarize their main characteristics, often with visual methods. By the name itself, we can get to know that it is a step in which we need to explore the data set.

2. Need For Exploratory Data Analysis

Exploratory Data Analysis is a crucial step before you jump to…

Every day use of pandas functions as a data scientist

Pandas is a python library used in data manipulation ( create, delete, and update the data).

It is one of the most commonly used libraries for data analysis in python. Pandas offer data structures and operations for manipulating numerical and time-series data.

Pandas First Steps

Install and import

Pandas is an easy package to install. Open up your terminal program (for Mac users) or command line (for PC users) and install it using either of the following commands:

conda install pandas

OR

pip install pandas

Alternatively, if you’re currently viewing this article in…

Anuganti Suresh

Working as Automotive design engineer. Actively looking for change the domain into Data Science. Certified from Simplilearn as “Data Scientist”.

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