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Machine Learning
1 üyeTakip
Modelleri değerlendirmede altın kural, modellerin asla eğitildikleri veriyle sınanmamasıdır.
XGBoostMachine Learning
It is worth noting that an optimized implementation of Gradient Boosting is available in the popular Python library XGBoost, which stands for Extreme Gradient Boosting. This package was initially developed by Tianqi Chen as part of the Distributed (Deep) Machine Learning Community (DMLC), and it aims to be extremely fast, scalable, and portable. In fact, XGBoost is often an important component of winning entries in ML competitions. XGBoost’s API is quite similar to Scikit-Learn’s:
import xgboost
xgb_reg = xgboost.XGBRegressor()
xgb_reg.fit(X_train, y_train)
y_pred = xgb_reg.predict(X_val)
XGBoost also offers several nice features, such as automatically taking care of early stopping:
xgb_reg.fit(X_train, y_train,
eval_set=[(X_val, y_val)], early_stopping_rounds=2)
y_pred = xgb_reg.predict(X_val)
You should definitely check it out!
Reklam
Some algorithms (such as SGD classifiers, Random Forest classifiers, and
naive Bayes classifiers) are capable of handling multiple classes natively.
Others (such as Logistic Regression or Support Vector Machine classifiers) are strictly binary classifiers. However, there are various strategies that you can use to perform multiclass classification with multiple binary classifiers.
There are many other models to forecast time series, such as weighted
moving average models or autoregressive integrated moving average
(ARIMA) models. Some of them require you to first remove the trend
and seasonality. For example, if you are studying the number of active
users on your website, and it is growing by 10% every month, you
would have to remove this trend from the time series. Once the model
is trained and starts making predictions, you would have to add the
trend back to get the final predictions. Similarly, if you are trying to
predict the amount of sunscreen lotion sold every month, you will
probably observe strong seasonality: since it sells well every summer,
a similar pattern will be repeated every year. You would have to
remove this seasonality from the time series, for example by
computing the difference between the value at each time step and the
value one year earlier (this technique is called differencing). Again,
after the model is trained and makes predictions, you would have to
add the seasonal pattern back to get the final predictions.
When using RNNs, it is generally not necessary to do all this, but it
may improve performance in some cases, since the model will not
have to learn the trend or the seasonality.
TREND AND SEASONALITY, satış tahminleri!