Self learning data evaluation in Python

Self learning data evaluation in Python

By : user2956773
Date : November 22 2020, 03:03 PM
it helps some times Your task is called classification and is a part of machine learning. This seems to be a very brief introduction to this field.
I don't know of a handy python library (I'm not saying there are none, but I don't use such things so I don't know of any), but some ML algorithms and classification models are very easy to implement yourself (e.g. k-NN, or linear classifier/regressor).
code :

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What is the best Evaluation Kit for Learning Embedded C/C++ Development?

What is the best Evaluation Kit for Learning Embedded C/C++ Development?

By : alburnett
Date : March 29 2020, 07:55 AM
this will help ST Micro has a very attractively priced (and packaged too) kit for their ARM Cortex-M3 based STM32 line. MSRP runs about US$35 for the STM32-PRIMER with 128x128 color LCD, MEMS accelerometer, push button, LEDs, USB, and some spare GPIOs all in a package that includes a battery and USB to JTAG debug connection. A GCC toolchain and a commercial debugger are supposed to come with it as well. I have one on order, and will try to remember to edit this answer to include a quick review after it arrives next week sometime.
They have a new model based on an STM32 with more FLASH and RAM on chip that also has a micro-SD card connector, and a larger LCD that includes a resistive touchscreen that runs just over $100 if you can find it in stock.
Machine Learning Algorithms Evaluation

Machine Learning Algorithms Evaluation

By : Charleemagnee
Date : March 29 2020, 07:55 AM
I hope this helps you . You can do both in R -- Dynamic Time Warping and Quadratic Discriminant Analysis.
How to make my Python Machine Learning model keep learning as I feed it more data each day?

How to make my Python Machine Learning model keep learning as I feed it more data each day?

By : xiaodong zhang
Date : March 29 2020, 07:55 AM
it helps some times These are the estimators in sklearn that support what you want to do and unfortunately for you Random Forest isn't one of them, so you will have to refit every time you add data.
If you insist on sticking with Random Forest, one option would be to reduce the number of features (based on the classifier you currently have) to increase the speed of refitting your classifier.
How set learning xgboost with evaluation set?

How set learning xgboost with evaluation set?

By : user1371397
Date : March 29 2020, 07:55 AM
it helps some times While using sklearn wrapper this is pretty easy to do for me this way: , Just need to specify parametrs correctly:
code :
params =   {
    #'objective' : 'gpu:reg:linear',
    'learning_rate': 0.02, 
    'gamma' : 0.3, 
    'min_child_weight' : 3,
    'nthread' : 15,
    'max_depth' : 30,
    'subsample' : 0.9, 
    'colsample_bytree' : 0.8, 
    'eval_metric' : "rmse",
    'num_boost_round' : 300,
    'max_leaves': 300

VALID = True
if VALID == True:
    X_train, X_valid, y_train, y_valid = train_test_split(
        X, y, test_size = 0.19, random_state=23)

    tr_data = xgb.DMatrix(X_train, y_train)
    va_data = xgb.DMatrix(X_valid, y_valid)

    #del X_train, X_valid, y_train, y_valid  ; gc.collect()

    watchlist = [(tr_data, 'train'), (va_data, 'valid')]

    model = xgb.train(params, tr_data, 300, watchlist, maximize=False, early_stopping_rounds = 30, verbose_eval=50)
human trace data for evaluation of reinforcement learning agent playing Atari?

human trace data for evaluation of reinforcement learning agent playing Atari?

By : user1382998
Date : March 29 2020, 07:55 AM
it should still fix some issue I'm not aware of that data being publicly available anywhere. Indeed, as far as I know all the papers that use such human start evaluations were written by the same lab/organization (DeepMind), so that doesn't rule out the possibility that DeepMind has kept the data internal and hasn't shared it with external researchers.
Note that the paper Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agents proposes a different (arguably better) approach for introducing the desired stochasticity in the environment to disincentivize an algorithm from simply memorizing strong sequences of actions. Their approach, referred to as sticky actions, is described in Section 5.2 of that paper. In 5.3 they also describe numerous disadvantages of other approaches, including disadvantages of the human starts approach.
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