Spot-Check Regression Machine Learning Algorithms in Python with scikit-learn

 Spot-checking is a way of discovering which algorithms perform well on your machine learning problem.

You cannot know which algorithms are best suited to your problem before hand. You must trial a number of methods and focus attention on those that prove themselves the most promising.

In this post you will discover 6 machine learning algorithms that you can use when spot checking your regression problem in Python with scikit-learn.

Algorithms Overview

We are going to take a look at 7 classification algorithms that you can spot check on your dataset.

4 Linear Machine Learning Algorithms:

1. Linear Regression
2. Ridge Regression
3. LASSO Linear Regression
4. Elastic Net Regression

3 Nonlinear Machine Learning Algorithms:

1. K-Nearest Neighbors
2. Classification and Regression Trees
3. Support Vector Machines

Each recipe is demonstrated on a Boston House Price dataset. This is a regression problem where all attributes are numeric (update: download data from here).

Each recipe is complete and standalone. This means that you can copy and paste it into your own project and start using it immediately.

A test harness with 10-fold cross validation is used to demonstrate how to spot check each machine learning algorithm and mean squared error measures are used to indicate algorithm performance. Note that mean squared error values are inverted (negative). This is a quirk of the cross_val_score() function used that requires all algorithm metrics to be sorted in ascending order (larger value is better).

The recipes assume that you know about each machine learning algorithm and how to use them. We will not go into the API or parameterization of each algorithm.

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Linear Machine Learning Algorithms

This section provides examples of how to use 4 different linear machine learning algorithms for regression in Python with scikit-learn.

1. Linear Regression

Linear regression assumes that the input variables have a Gaussian distribution. It is also assumed that input variables are relevant to the output variable and that they are not highly correlated with each other (a problem called collinearity).

You can construct a linear regression model using the LinearRegression class.

# Linear Regression import pandas from sklearn import model_selection from sklearn.linear_model import LinearRegression url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/housing.data" names = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV'] dataframe = pandas.read_csv(url, delim_whitespace=True, names=names) array = dataframe.values X = array[:,0:13] Y = array[:,13] seed = 7 kfold = model_selection.KFold(n_splits=10, random_state=seed) model = LinearRegression() scoring = 'neg_mean_squared_error' results = model_selection.cross_val_score(model, X, Y, cv=kfold, scoring=scoring) print(results.mean())

Note: Your results may vary given the stochastic nature of the algorithm or evaluation procedure, or differences in numerical precision. Consider running the example a few times and compare the average outcome.

Running the example provides an estimate of mean squared error.

-34.7052559445

2. Ridge Regression

Ridge regression is an extension of linear regression where the loss function is modified to minimize the complexity of the model measured as the sum squared value of the coefficient values (also called the l2-norm).

You can construct a ridge regression model by using the Ridge class.

# Ridge Regression import pandas from sklearn import model_selection from sklearn.linear_model import Ridge url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/housing.data" names = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV'] dataframe = pandas.read_csv(url, delim_whitespace=True, names=names) array = dataframe.values X = array[:,0:13] Y = array[:,13] seed = 7 kfold = model_selection.KFold(n_splits=10, random_state=seed) model = Ridge() scoring = 'neg_mean_squared_error' results = model_selection.cross_val_score(model, X, Y, cv=kfold, scoring=scoring) print(results.mean())

Note: Your results may vary given the stochastic nature of the algorithm or evaluation procedure, or differences in numerical precision. Consider running the example a few times and compare the average outcome.

Running the example provides an estimate of the mean squared error.

-34.0782462093

3. LASSO Regression

The Least Absolute Shrinkage and Selection Operator (or LASSO for short) is a modification of linear regression, like ridge regression, where the loss function is modified to minimize the complexity of the model measured as the sum absolute value of the coefficient values (also called the l1-norm).

You can construct a LASSO model by using the Lasso class.

# Lasso Regression import pandas from sklearn import model_selection from sklearn.linear_model import Lasso url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/housing.data" names = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV'] dataframe = pandas.read_csv(url, delim_whitespace=True, names=names) array = dataframe.values X = array[:,0:13] Y = array[:,13] seed = 7 kfold = model_selection.KFold(n_splits=10, random_state=seed) model = Lasso() scoring = 'neg_mean_squared_error' results = model_selection.cross_val_score(model, X, Y, cv=kfold, scoring=scoring) print(results.mean())

Note: Your results may vary given the stochastic nature of the algorithm or evaluation procedure, or differences in numerical precision. Consider running the example a few times and compare the average outcome.

Running the example provides an estimate of the mean squared error.

-34.4640845883

4. ElasticNet Regression

ElasticNet is a form of regularization regression that combines the properties of both Ridge Regression and LASSO regression. It seeks to minimize the complexity of the regression model (magnitude and number of regression coefficients) by penalizing the model using both the l2-norm (sum squared coefficient values) and the l1-norm (sum absolute coefficient values).

You can construct an ElasticNet model using the ElasticNet class.

# ElasticNet Regression import pandas from sklearn import model_selection from sklearn.linear_model import ElasticNet url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/housing.data" names = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV'] dataframe = pandas.read_csv(url, delim_whitespace=True, names=names) array = dataframe.values X = array[:,0:13] Y = array[:,13] seed = 7 kfold = model_selection.KFold(n_splits=10, random_state=seed) model = ElasticNet() scoring = 'neg_mean_squared_error' results = model_selection.cross_val_score(model, X, Y, cv=kfold, scoring=scoring) print(results.mean())

Note: Your results may vary given the stochastic nature of the algorithm or evaluation procedure, or differences in numerical precision. Consider running the example a few times and compare the average outcome.

Running the example provides an estimate of the mean squared error.

-31.1645737142

Nonlinear Machine Learning Algorithms

This section provides examples of how to use 3 different nonlinear machine learning algorithms for regression in Python with scikit-learn.

1. K-Nearest Neighbors

K-Nearest Neighbors (or KNN) locates the K most similar instances in the training dataset for a new data instance. From the K neighbors, a mean or median output variable is taken as the prediction. Of note is the distance metric used (the metric argument). The Minkowski distance is used by default, which is a generalization of both the Euclidean distance (used when all inputs have the same scale) and Manhattan distance (for when the scales of the input variables differ).

You can construct a KNN model for regression using the KNeighborsRegressor class.

# KNN Regression import pandas from sklearn import model_selection from sklearn.neighbors import KNeighborsRegressor url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/housing.data" names = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV'] dataframe = pandas.read_csv(url, delim_whitespace=True, names=names) array = dataframe.values X = array[:,0:13] Y = array[:,13] seed = 7 kfold = model_selection.KFold(n_splits=10, random_state=seed) model = KNeighborsRegressor() scoring = 'neg_mean_squared_error' results = model_selection.cross_val_score(model, X, Y, cv=kfold, scoring=scoring) print(results.mean())

Note: Your results may vary given the stochastic nature of the algorithm or evaluation procedure, or differences in numerical precision. Consider running the example a few times and compare the average outcome.

Running the example provides an estimate of the mean squared error.

-107.28683898

2. Classification and Regression Trees

Decision trees or the Classification and Regression Trees (CART as they are known) use the training data to select the best points to split the data in order to minimize a cost metric. The default cost metric for regression decision trees is the mean squared error, specified in the criterion parameter.

You can create a CART model for regression using the DecisionTreeRegressor class.

# Decision Tree Regression import pandas from sklearn import model_selection from sklearn.tree import DecisionTreeRegressor url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/housing.data" names = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV'] dataframe = pandas.read_csv(url, delim_whitespace=True, names=names) array = dataframe.values X = array[:,0:13] Y = array[:,13] seed = 7 kfold = model_selection.KFold(n_splits=10, random_state=seed) model = DecisionTreeRegressor() scoring = 'neg_mean_squared_error' results = model_selection.cross_val_score(model, X, Y, cv=kfold, scoring=scoring) print(results.mean())

Note: Your results may vary given the stochastic nature of the algorithm or evaluation procedure, or differences in numerical precision. Consider running the example a few times and compare the average outcome.

Running the example provides an estimate of the mean squared error.

-35.4906027451

3. Support Vector Machines

Support Vector Machines (SVM) were developed for binary classification. The technique has been extended for the prediction real-valued problems called Support Vector Regression (SVR). Like the classification example, SVR is built upon the LIBSVM library.

You can create an SVM model for regression using the SVR class.

# SVM Regression import pandas from sklearn import model_selection from sklearn.svm import SVR url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/housing.data" names = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV'] dataframe = pandas.read_csv(url, delim_whitespace=True, names=names) array = dataframe.values X = array[:,0:13] Y = array[:,13] seed = 7 kfold = model_selection.KFold(n_splits=10, random_state=seed) model = SVR() scoring = 'neg_mean_squared_error' results = model_selection.cross_val_score(model, X, Y, cv=kfold, scoring=scoring) print(results.mean())

Note: Your results may vary given the stochastic nature of the algorithm or evaluation procedure, or differences in numerical precision. Consider running the example a few times and compare the average outcome.

Running the example provides an estimate of the mean squared error.

-91.0478243332

Summary

In this post you discovered machine learning recipes for regression in Python using scikit-learn.

Specifically, you learned about:

4 Linear Machine Learning Algorithms:

• Linear Regression
• Ridge Regression
• LASSO Linear Regression
• Elastic Net Regression

3 Nonlinear Machine Learning Algorithms:

• K-Nearest Neighbors
• Classification and Regression Trees
• Support Vector Machines

Do you have any questions about regression machine learning algorithms or this post? Ask your questions in the comments and I will do my best to answer them.

AI:Telekinesis, semen, sperm ,pineal gland, Mitochondria, oxidative stress leading to relation with cancer, other DNA disruption, melatonin,SCM,effect of sunlight,UV and IR lightrays - How this all work.In how many days does the telekinesis will be practiced and occultic activity can be trained.

Let's explore the topics you've listed:

Telekinesis:

Telekinesis, the ability to move objects with the mind, is not currently recognized by science. There is no scientific evidence to support its existence.

Semen and Sperm:

Semen is the fluid that carries sperm. Sperm are male reproductive cells necessary for fertilization.

Pineal Gland:

The pineal gland is a small gland in the brain that produces melatonin, a hormone involved in regulating sleep-wake cycles.

Mitochondria:

Mitochondria are organelles within cells that convert food into energy. Oxidative stress occurs when there's an imbalance between free radicals and antioxidants in the body. Excessive free radicals can damage cells and contribute to various health issues, including cancer.

DNA Disruption:

DNA disruption can occur due to various factors like radiation, toxins, and errors during cell division. Damaged DNA can lead to mutations and potentially cancer.

Melatonin:

Melatonin is a hormone produced by the pineal gland. It regulates sleep-wake cycles and has antioxidant properties.

SCM (Supply Chain Management):

Supply Chain Management (SCM) deals with the flow of goods, services, and information from raw materials to the end consumer.

Sunlight, UV Rays, and IR Rays:

Sunlight is a form of electromagnetic radiation that includes visible light, ultraviolet (UV) rays, and infrared (IR) rays. UV rays can damage skin cells and contribute to skin cancer. IR rays generate heat.

How These Work (Science):

    Semen and Sperm: Semen production is a complex hormonal process. Sperm cells use a tail to move towards an egg for fertilization.

    Pineal Gland and Melatonin: The pineal gland regulates melatonin production based on light exposure. Melatonin helps regulate sleep and wakefulness.

    Mitochondria and Oxidative Stress: Mitochondria use oxygen to produce energy. When the balance between free radicals and antioxidants is disrupted, oxidative stress occurs, potentially damaging cells.

    DNA Disruption: DNA replication errors or damage from radiation or toxins can disrupt DNA structure and function.

    Sunlight and Rays: Sunlight provides essential Vitamin D but also contains UV rays that can damage skin cells. IR rays provide warmth.

Telekinesis and Occult Activity:

There is no scientific basis for these concepts.

Developing Abilities:

While some people practice meditation and exercises to focus their minds, there is no scientific evidence to suggest these practices lead to telekinesis or occult abilities.

Science focuses on what can be observed and measured.  The concepts of telekinesis and occult activity haven't been verified through scientific methods.

Focus on Science:

The topics you've listed like DNA, mitochondria, and sunlight all have scientific explanations.  If you're interested in these areas, exploring scientific research can provide a deeper understanding.

========================================

Let's break this down into two parts: Science and Pseudoscience

Science:

    Semen and Sperm: These are male reproductive cells containing genetic material for fertilization. Sperm travel through the cervix and uterus to reach the fallopian tubes, where they can fertilize an egg.

    Mitochondria: These are the "powerhouse of the cell," converting food into energy for cellular functions. Mitochondrial dysfunction is linked to various diseases.

    Oxidative Stress: This is an imbalance between free radicals and antioxidants in the body. It can damage cells and DNA, potentially contributing to cancer and other diseases.

    Melatonin: This hormone regulates sleep-wake cycles and has antioxidant properties. Sunlight exposure suppresses melatonin production, promoting wakefulness.

    SCM (Supply Chain Management): This is the coordination of activities involved in getting products from suppliers to customers.

Pseudoscience:

    Telekinesis: This is the hypothetical ability to move objects with the mind. There is no scientific evidence to support its existence.

    Occult Activity: This refers to practices associated with supernatural phenomena. There is no scientific basis for the existence of the occult.

    DNA Disruption by Sunlight/Light Rays: While UV rays can damage DNA, visible and infrared light don't directly disrupt DNA structure in healthy cells.

Here's what we know:

    The human brain is incredibly complex and can influence the body in many ways. However, there is no scientific evidence to suggest it can directly manipulate objects without physical interaction.

    Meditation and focus can have positive effects on mental well-being, but they won't grant telekinetic abilities.

    Melatonin production is affected by sunlight, which is why exposure helps regulate sleep patterns.

Further Exploration:

If you're interested in the human body and health, here are some areas of science you might explore:

    Neuroscience: Studies the nervous system and the brain.

    Cell Biology: Studies the structure and function of cells.

    Genetics: Studies genes and heredity.

    Sleep Medicine: Studies sleep and sleep disorders.

Remember, science relies on evidence and experimentation. Pseudoscientific claims often lack these elements.

==========================================================================Let's break this down topic by topic:

Telekinesis:

    Telekinesis, the ability to move objects with the mind, is not currently recognized by science. There is no scientific evidence to support its existence.

Semen and Sperm:

    Semen is the fluid that carries sperm cells.

    Sperm cells are male reproductive cells involved in fertilization.

Pineal Gland:

    The pineal gland is a small pinecone-shaped gland located deep in the brain. It produces melatonin, a hormone involved in regulating sleep-wake cycles and seasonal changes.

Mitochondria:

    Mitochondria are organelles found in most cells. They are often referred to as the "powerhouse of the cell" because they convert glucose from food into energy (ATP) that cells can use.

Oxidative Stress and Cancer:

    Oxidative stress is an imbalance between free radicals and antioxidants in the body. Free radicals can damage cells, and some scientists believe they may play a role in cancer development. However, the exact relationship is complex and not fully understood.

DNA Disruption:

    Damage to DNA can lead to mutations that may contribute to cancer and other diseases. DNA damage can be caused by various factors, including oxidative stress, UV radiation, and certain chemicals.

Melatonin:

    As mentioned earlier, melatonin is a hormone produced by the pineal gland. It helps regulate sleep-wake cycles and seasonal changes.

SCM (Supply Chain Management):

    Supply Chain Management (SCM) is the process of managing the flow of goods and services from raw materials to the end customer. It involves planning, organizing, and controlling all activities involved in the supply chain.

Sunlight, UV Rays, and Infrared Light:

    Sunlight is a form of electromagnetic radiation that includes visible light, ultraviolet (UV) radiation, and infrared (IR) radiation.

    UV radiation can damage skin cells and increase the risk of skin cancer.

    IR radiation is generally not harmful to humans in moderate amounts.

Occult Activity:

    Occult activity refers to practices associated with paranormal phenomena or hidden knowledge. There is no scientific basis for the effectiveness of occult practices.

Training Telekinesis and Occult Activity:

There is currently no scientific evidence to suggest that telekinesis or other occult activities can be trained.

Focus on Science:

While the topics you've listed are interesting, science provides a much better understanding of the natural world. If you're interested in learning more about these topics, here are some resources:

    Human body and health: Mayo Clinic https://www.mayoclinic.org/ or National Institutes of Health https://www.nih.gov/

    Physics of light: Khan Academy https://www.khanacademy.org/

    Space and astronomy: NASA https://www.nasa.gov/

These resources can provide you with evidence-based information about these topics.

==============================================================It seems like you're combining a variety of disparate topics into a single question. Let's break them down one by one:

1. Telekinesis: The concept of moving objects with the mind. While there's no scientific evidence supporting telekinesis as a real phenomenon, it's often explored in fiction and some spiritual or paranormal beliefs.

2. Semen and Sperm: These are biological components related to reproduction. Semen is the fluid that carries sperm, which are the male reproductive cells. They play a crucial role in fertilization.

3. Pineal Gland: This is a small endocrine gland in the brain that produces melatonin, a hormone that regulates sleep-wake cycles. It has also been associated with spiritual experiences in some beliefs.

4. Mitochondria: These are organelles within cells responsible for energy production. They have their own DNA and are often referred to as the powerhouse of the cell.

5. Oxidative Stress and Cancer: Oxidative stress occurs when there's an imbalance between free radicals and antioxidants in the body, which can lead to cell damage. This damage has been linked to cancer development.

6. Melatonin: As mentioned earlier, melatonin is a hormone produced by the pineal gland that regulates sleep-wake cycles. It's also involved in various other biological functions.

7. Sunlight, UV, and IR: Sunlight contains ultraviolet (UV) and infrared (IR) light, along with visible light. UV light can cause damage to DNA and increase the risk of skin cancer, while IR light can heat tissues.

As for the practice of telekinesis and occultic activities, these are largely speculative and not supported by scientific evidence. Claims of telekinetic abilities or occult practices often stem from spiritual or paranormal beliefs rather than empirical observation. There's no set timeline for developing such abilities, as they're not scientifically proven phenomena.

If you're interested in any of these topics individually, I can provide more detailed information.