Machine-Learning-with-Python | One Small Course | Learning library
kandi X-RAY | Machine-Learning-with-Python Summary
kandi X-RAY | Machine-Learning-with-Python Summary
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- Train a neural network
- 2D convolutional layer
- Convolutional convolutional network
- Max pooling op
- Create feature sets and labels
- Extract features from a sample
- Create a lexicon from a text file
- Calculates the coefficient of the determination
- Computes the squared error between two lines
- Use neural network
- Creates a neural network model
- Calculate best fit and intercept
- Predict the classification for the given data
- Predict given features
- Calculate the center of the centroids
- Calculates k nearest neighbors
- Fit the model to the data
- This method handles the non - numerical data
- Visualize features
- Fit the clustering
- Calculates the best fit
Machine-Learning-with-Python Key Features
Machine-Learning-with-Python Examples and Code Snippets
Community Discussions
Trending Discussions on Machine-Learning-with-Python
QUESTION
I have a trained machine learning model to predict one metric of my data in firestore.
I have inserted data into bigquery to train the model and ones it is trained I want to deploy it to make predictions and insert this predictions again in firestore.
I have been reading a lot how to do this and finally I have found a way to do it: https://angularfirebase.com/lessons/serverless-machine-learning-with-python-and-firebase-cloud-functions/
I have a few questions of this guide:
- In the 4th step of this guide he save the model in firebase_admin storage. Why in firebase_admin storage and not in a google cloud storage?
- He deploy the model in Google cloud ML engine. Why he do that? what advantadges I have to deploy the model there instead of save the model in Google cloud storage and after that call it in cloud functions? or it is necessary to do this step?
- Once he deploy this model in Google cloud ML engine I can call it in a cloud fuction in python to run the model whatever the trigger I choose?
ANSWER
Answered 2019-Oct-18 at 11:491) You can use Cloud Storage, as well. In the tutorial he suggests a way of achieving something, but it is not the single absolute way.
2) You can also do what you have said, but the way from that tutorial is better, I would say. You have the advantage of exposing your model as an API endpoint. This way you will have less work to do in the Cloud Functions code which is good.
3) I am not sure if I understood this question. You should use a HTTP trigger since you are trying to hit an API endpoint.
QUESTION
I'm new to machine learning and would like to setup a little sample using the k-nearest-Neighbor-method
with the Python library Scikit
.
Transforming and fitting the data works fine but I can't figure out how to plot a graph showing the datapoints surrounded by their "neighborhood".
The dataset I'm using looks like that:
So there are 8 features, plus one "outcome" column.
From my understanding, I get an array, showing the euclidean-distances
of all datapoints, using the kneighbors_graph from Scikit
.
So my first attempt was "simply" plotting that matrix that I get as a result from that method. Like so:
ANSWER
Answered 2019-May-22 at 06:35Try these two simple pieces of code, both plots a 3D graph with 6 variables,plotting a higher dimensional data is always difficult but you can play with it & check if it can be tweaked to get your desired neighbourhood graph.
First one is pretty intuitive but it gives you random rays or boxes(depends on your number of variables) you cannot plot more than 6 variables it always threw error to me on using more dimensions, but you will have to be creative enough to somehow use the other two variables. It will make sense when you'll see the second piece of code.
first piece of code
QUESTION
I'm running the following code from github, but I'm getting an error. What's wrong?
Cell:
...ANSWER
Answered 2019-Apr-09 at 00:43Solved the problem adding the methods below, which apparently transform train and test objects to numpy arrays. Is that correct?
QUESTION
The regularization parameter C in logistic regression (see http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) is used allow the function to be fitted to be well defined and avoid either overfitting or problems with step functions (see https://datascience.stackexchange.com/questions/10805/does-scikit-learn-use-regularization-by-default/10806).
However, regularization in logistic regression should only concern the weights for the features, not the intercept (also explained here: http://aimotion.blogspot.com/2011/11/machine-learning-with-python-logistic.html)
But is seems that sklearn.linear_model.LogisticRegression actually regularizes the intercept as well. Here is why:
1) Conside above link carefully (https://datascience.stackexchange.com/questions/10805/does-scikit-learn-use-regularization-by-default/10806): the sigmod is moved slightly to the left, closer to the intercept 0.
2) I tried to fit data points with a logistic curve and a manual maximum likelihood function. Including the intercept into the L2 norm gives identical results as sklearn's function.
Two questions please:
1) Did I get this wrong, is this a bug, or is there a well-justified reason for regularizing the intercept?
2) Is there a way to use sklearn and specify to regularize all parameters except the intercepts?
Thanks!
...ANSWER
Answered 2017-Nov-02 at 12:20scikit-learn has default regularized logistic regression.
The change in intercept_scaling
parameter value in sklearn.linear_model.LogisticRegression
has similar effect on the result if only C
parameter is changed.
In case of modification in intercept_scaling
parameter, regularization has an impact on the estimation of bias in logistic regression. When this parameter's value is on higher side then the regularization impact on bias is reduced. Per official documentation:
The intercept becomes
intercept_scaling * synthetic_feature_weight
.
Note! the synthetic feature weight is subject to l1/l2 regularization as all other features. To lessen the effect of regularization on synthetic feature weight (and therefore on the intercept) intercept_scaling has to be increased.
Hope it helps!
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Install Machine-Learning-with-Python
You can use Machine-Learning-with-Python like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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