Hi,
In this post, we will learn how machine learning algorithm work, here we go through basic concepts of all the machine learning algorithms and how to fit and predict train and test data in machine learning.
Types of ML Algorithms:
ML Algorithms divided into three categories -
1. Supervised Learning
2. Unsupervised Learning
3. Reinforcement Learning
Supervised Learning
It consists of the target variable (or dependent variable) which is to be predicted from a given set of predictors (independent variables). The training process continues until the model achieves a desired level of accuracy on the training data.
Types of Supervised Learning
Regression,
Decision Tree,
Random Forest,
KNN,
Logistic Regression, etc.
Unsupervised Learning
This algorithm work without having any target or outcome variable to predict. It is used for the clustering population in different groups, which is used for segmenting customers in different groups.
Types of Unsupervised Learning
Apriori algorithm,
K-means
Reinforcement Learning
This algorithm used from past experience and tries to capture the best possible knowledge to find accurate decisions.
Types of Reinforcement Learning
Markov Decision Process
Linear Regression
# importing required libraries
import pandas as pd
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
Logistic Regression
Decision Tree
SVM (Support Vector Machine)
Naive Bayes
KNN (k- Nearest Neighbors)
K-Means
Metrics to Evaluate your Machine Learning Algorithm
There are different types of metrics used to evaluate ML Algorithms :
Classification Accuracy
Logarithmic Loss
Confusion Matrix
Area under Curve
F1 Score
Mean Absolute Error
Mean Squared Error
Here we will create basic train and test data and fit it into different models, you can also try it itself, here we some changes are made the first one is set appropriate libraries and fit data.
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